Academic literature on the topic 'Pixel-Object classification'
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Journal articles on the topic "Pixel-Object classification"
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Bernardini, A., E. Frontoni, E. S. Malinverni, A. Mancini, A. N. Tassetti, and P. Zingaretti. "Pixel, object and hybrid classification comparisons." Journal of Spatial Science 55, no. 1 (2010): 43–54. http://dx.doi.org/10.1080/14498596.2010.487641.
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Makinde, Esther Oluwafunmilayo, Ayobami Taofeek Salami, James Bolarinwa Olaleye, and Oluwapelumi Comfort Okewusi. "Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria." Geoinformatics FCE CTU 15, no. 2 (2016): 59–70. http://dx.doi.org/10.14311/gi.15.2.5.
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Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based on segments was done by a nearest neighbour classifier. In the pixel-based classification, the spectral angle mapper was used to classify the images. The user accuracy for each class using object based classification were 98.31% for waterbody, 92.31% for vegetation, 86.67% for bare soil and 90.57% for Built up while the user accuracy for the pixel based classification were 98.28% for waterbody, 84.06% for Vegetation 86.36% and 79.41% for Built up. These classification techniques were subjected to accuracy assessment and the overall accuracy of the Object based classification was 94.47%, while that of Pixel based classification yielded 86.64%. The result of classification and accuracy assessment show that the object-based approach gave more accurate and satisfying results
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Martínez Prentice, Ricardo, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp. "Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands." Remote Sensing 13, no. 18 (2021): 3669. http://dx.doi.org/10.3390/rs13183669.
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High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.
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Sutanto, Ahmad, Bambang Trisakti, and Aniati Murni Arymurthy. "PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN." Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital 11, no. 1 (2014): 63–75. https://doi.org/10.30536/inderaja.v11i1.3300.
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Utilization of remote sensing data for land mapping has long been developed. In Indonesia, as a tropical region, the cloud becomes a classic problem in observing the Earth’s surface using optical remotely sensor satellite. Synthetic Aperture Radar (SAR) sensor satellite has the ability to penetrate clouds so it can solve cloud cover problems. In this study, the ALOS PALSAR data were used to assess object-based and pixel-based classification techniques. This data was chosen due to its capacity for object recognition based on backscatter characteristics. Object-based classification using the methods of Statistical Region Merging (SRM) for the object segmentation process and Support Vector Machine (SVM) for the classification process, whereas the pixel-based classification using SVM method. In the classification stage, several features of Target Decomposition and Image Decomposition of ALOS PALSAR data have been tested. The accuracy assessment of the classification was conducted using confusion matrix of the Region of Interest (ROI) data using the QuickBird data. Implementation of the object-based classification produced better result comparing to pixel-based classification. The number of optimal features is seven which consisted of three features Freeman Decomposition (Red, Green, Blue), Entropy, Alpha Angle, Anisotropy and Normalized Difference Polarization Index (NDPI). Overall accuracy reached 73.64% for the result of the object-based classification and 62.6% for the pixel-based classification.
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He, Ziqiang, Shaosheng Dai, and Jinsong Liu. "Single-pixel object classification using ordered illumination patterns." Optics Communications 573 (December 2024): 131023. http://dx.doi.org/10.1016/j.optcom.2024.131023.
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Liu, Yanzhu, Yanan Wang, and Adams Wai Kin Kong. "Pixel-wise ordinal classification for salient object grading." Image and Vision Computing 106 (February 2021): 104086. http://dx.doi.org/10.1016/j.imavis.2020.104086.
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Deur, Martina, Mateo Gašparović, and Ivan Balenović. "An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery." Remote Sensing 13, no. 10 (2021): 1868. http://dx.doi.org/10.3390/rs13101868.
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Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.
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Kang, Min Jo, Victor Mesev, and Won Kyung Kim. "Measurements of Impervious Surfaces - per-pixel, sub-pixel, and object-oriented classification -." Korean Journal of Remote Sensing 31, no. 4 (2015): 303–19. http://dx.doi.org/10.7780/kjrs.2015.31.4.3.
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Endo, Yutaka, and Gai Nakajima. "Compressive phase object classification using single-pixel digital holography." Optics Express 30, no. 15 (2022): 28057. http://dx.doi.org/10.1364/oe.463395.
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A single-pixel camera (SPC) is a computational imaging system that obtains compressed signals of a target scene using a single-pixel detector. The compressed signals can be directly used for image classification, thereby bypassing image reconstruction, which is computationally intensive and requires a high measurement rate. Here, we extend this direct inference to phase object classification using single-pixel digital holography (SPDH). Our method obtains compressed measurements of target complex amplitudes using SPDH and trains a classifier using those measurements for phase object classification. Furthermore, we present a joint optimization of the sampling patterns used in SPDH and a classifier to improve classification accuracy. The proposed method successfully classified phase object images of handwritten digits from the MNIST database, which is challenging for SPCs that can only capture intensity images.
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Powar, Sudhir K., Sachin S. Panhalkar, and Abhijit S. Patil. "An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques." Geomatics and Environmental Engineering 16, no. 2 (2022): 61–75. http://dx.doi.org/10.7494/geom.2022.16.2.61.
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Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification.
Dissertations / Theses on the topic "Pixel-Object classification"
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Ali, Fadi. "Urban classification by pixel and object-based approaches for very high resolution imagery." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23993.
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Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity. Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.
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Porter, Sarah Ann. "Land cover study in Iowa: analysis of classification methodology and its impact on scale, accuracy, and landscape metrics." Thesis, University of Iowa, 2011. https://ir.uiowa.edu/etd/1169.
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For landscapes dominated by agriculture, land cover plays an important role in the balance between anthropogenic and natural forces. Therefore, the objective of this thesis is to describe two different methodologies that have been implemented to create high-resolution land cover classifications in a dominant agricultural landscape. First, an object-based segmentation approach will be presented, which was applied to historic, high resolution, panchromatic aerial photography. Second, a traditional per-pixel technique was applied to multi-temporal, multispectral, high resolution aerial photography, in combination with light detection and ranging (LIDAR) and independent component analysis (ICA). A critical analysis of each approach will be discussed in detail, as well as the ability of each methodology to generate landscape metrics that can accurately characterize the quality of the landscape. This will be done through the comparison of various landscape metrics derived from the different classifications approaches, with a goal of enhancing the literature concerning how these metrics vary across methodologies and across scales. This is a familiar problem encountered when analyzing land cover datasets over time, which are often at different scales or generated using different methodologies. The diversity of remotely sensed imagery, including varying spatial resolutions, landscapes, and extents, as well as the wide range of spatial metrics that can be created, has generated concern about the integrity of these metrics when used to make inferences about landscape quality. Finally, inferences will be made about land cover and land cover change dynamics for the state of Iowa based on insight gained throughout the process.
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Grift, Jeroen. "Forest Change Mapping in Southwestern Madagascar using Landsat-5 TM Imagery, 1990 –2010." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-22606.
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The main goal of this study was to map and measure forest change in the southwestern part of Madagascar near the city of Toliara in the period 1990-2010. Recent studies show that forest change in Madagascar on a regional scale does not only deal with forest loss, but also with forest growth However, it is unclear how the study area is dealing with these patterns. In order to select the right classification method, pixel-based classification was compared with object-based classification. The results of this study shows that the object-based classification method was the most suitable method for this landscape. However, the pixel-based approaches also resulted in accurate results. Furthermore, the study shows that in the period 1990–2010, 42% of the forest cover disappeared and was converted into bare soil and savannahs. Next to the change in forest, stable forest regions were fragmented. This has negative effects on the amount of suitable habitats for Malagasy fauna. Finally, the scaling structure in landscape patches was investigated. The study shows that the patch size distribution has long-tail properties and that these properties do not change in periods of deforestation.
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Yokum, Hannah Elizabeth. "Understanding Community and Ecophysiology of Plant Species on the Colorado Plateau." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/7211.
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The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.
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Abidi, Azza. "Investigating Deep Learning and Image-Encoded Time Series Approaches for Multi-Scale Remote Sensing Analysis in the context of Land Use/Land Cover Mapping." Electronic Thesis or Diss., Université de Montpellier (2022-....), 2024. http://www.theses.fr/2024UMONS007.
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Cette thèse explore le potentiel de l'apprentissage automatique pour améliorer la cartographie de modèles complexes d'utilisation des sols et de la couverture terrestre à l'aide de données d'observation de la Terre. Traditionnellement, les méthodes de cartographie reposent sur la classification et l'interprétation manuelles des images satellites, qui sont sujettes à l'erreur humaine. Cependant, l'application de l'apprentissage automatique, en particulier par le biais des réseaux neuronaux, a automatisé et amélioré le processus de classification, ce qui a permis d'obtenir des résultats plus objectifs et plus précis. En outre, l'intégration de données de séries temporelles d'images satellitaires (STIS) ajoute une dimension temporelle aux informations spatiales, offrant une vue dynamique de la surface de la Terre au fil du temps. Ces informations temporelles sont essentielles pour une classification précise et une prise de décision éclairée dans diverses applications. Les informations d'utilisation des sols et de la couverture terrestre précises et actuelles dérivées des données STIS sont essentielles pour guider les initiatives de développement durable, la gestion des ressources et l'atténuation des risques environnementaux. Le processus de cartographie de d'utilisation des sols et de la couverture terrestre à l'aide du l'apprentissage automatique implique la collecte de données, le prétraitement, l'extraction de caractéristiques et la classification à l'aide de divers algorithmes l'apprentissage automatique . Deux stratégies principales de classification des données STIS ont été proposées : l'approche au niveau du pixel et l'approche basée sur l'objet. Bien que ces deux approches se soient révélées efficaces, elles posent également des problèmes, tels que l'incapacité à capturer les informations contextuelles dans les approches basées sur les pixels et la complexité de la segmentation dans les approches basées sur les objets.Pour relever ces défis, cette thèse vise à mettre en œuvre une métho basée sur des informations multi-échelles pour effectuer la classification de l'utilisation des terres et de la couverture terrestre, en couplant les informations spectrales et temporelles par le biais d'une méthodologie combinée pixel-objet et en appliquant une approche méthodologique pour représenter efficacement les données multi-variées SITS dans le but de réutiliser la grande quantité d'avancées de la recherche proposées dans le domaine de la vision par ordinateur<br>In this thesis, the potential of machine learning (ML) in enhancing the mapping of complex Land Use and Land Cover (LULC) patterns using Earth Observation data is explored. Traditionally, mapping methods relied on manual and time-consuming classification and interpretation of satellite images, which are susceptible to human error. However, the application of ML, particularly through neural networks, has automated and improved the classification process, resulting in more objective and accurate results. Additionally, the integration of Satellite Image Time Series(SITS) data adds a temporal dimension to spatial information, offering a dynamic view of the Earth's surface over time. This temporal information is crucial for accurate classification and informed decision-making in various applications. The precise and current LULC information derived from SITS data is essential for guiding sustainable development initiatives, resource management, and mitigating environmental risks. The LULC mapping process using ML involves data collection, preprocessing, feature extraction, and classification using various ML algorithms. Two main classification strategies for SITS data have been proposed: pixel-level and object-based approaches. While both approaches have shown effectiveness, they also pose challenges, such as the inability to capture contextual information in pixel-based approaches and the complexity of segmentation in object-based approaches.To address these challenges, this thesis aims to implement a method based on multi-scale information to perform LULC classification, coupling spectral and temporal information through a combined pixel-object methodology and applying a methodological approach to efficiently represent multivariate SITS data with the aim of reusing the large amount of research advances proposed in the field of computer vision
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Lubbe, Minette. "Comparison of pixel-based and object-oriented classification approaches for detection of camouflaged objects." Thesis, 2012. http://hdl.handle.net/10210/4455.
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M.A.<br>The dissertation topic is the comparison of pixel-based and object-oriented image analysis approaches for camouflaged object detection research. A camouflage field trial experiment was conducted during 2004. For the experiment, 11 military vehicles were deployed along a tree line and in an open field. A subset of the vehicles was deployed with a variety of experimental camouflage nets and a final subset was left uncovered. The reason for deploying the camouflaged objects in the open without the use of camouflage principals was to create a baseline for future measurements. During the next experimental deployment, the camouflaged targets will be deployed according to camouflage principals. It must be emphasised that this is an experimental deployment and not an operational deployment. Unobstructed entity panels were also deployed and served as calibration entities. During the trial, both airborne (colour aerial photography) and space borne (multi-spectral QuickBird) imagery were acquired over the trial sites, and extensive calibration and ground truthing activities were conducted in support of these acquisitions. This study further describes the processing that was done after acquisition of the datasets. The goal is to determine which classification techniques are the most effective in the detection of camouflaged objects. This will also show how well or poor the SANDF camouflage nets and paint potentially perform against air and space based sensors on the one hand and classification techniques on the other. Using this information, DPSS can identify the nets and paints that need to be investigated for future enhancements (e.g. colour selection, colour combinations, base material, camouflage patterns, entity shapes, entity textures, etc.). The classification techniques to be used against SANDF camouflaged objects will also give an indication of their performance against camouflaged advesarial forces in the future.
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Wang, Wenyi, and 王文宜. "A study of Region Object-oriented Classification & pixel-based Classification on the Remote Sensing image of the landslide area of Wan Dan Reservoir." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/21776349064403022439.
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碩士<br>嶺東科技大學<br>數位媒體設計研究所<br>100<br>In the image classification, it generally used pixel-based classification model to extract information of image. The results of by pixel-based algorithm can induce Salt-and-Pepper Effect. Therefore, this study purposed a region-based model of Region Object-oriented Classification (ROC) to extract landslide image information. The surface information from the Wan Da reservoir area is collected and studied. Region Object-oriented Classification (ROC) is used to classify the landslide area. We collected different spectrum with several texture information to analyze the surrounding area of Wan Da reservoir. Entropy based classification is used as a classifier to determine the landslide/non-landslide area. Various parameters of S (similarity) and A (area) are used and then the best combinations are found. In the parallel study, we developed a pixel based classification through the calculation on the entropy for simple comparison. The relations of occurrence vs. non-occurrence of landslide with regards to attributes of land surface are studied. Thus, this could be of help to manage the recover on the landslide area.
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Diyan, Mohammad Abdullah Abu. "Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh." Master's thesis, 2011. http://hdl.handle.net/10362/5624.
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.<br>This study investigates the potential of using very high resolution (VHR) QuickBird data to
conduct vegetation classification of the Sundarban mangrove forest in Bangladesh and
compares the results with Landsat TM data. Previous studies of vegetation classification in
Sundarban involved Landsat images using pixel-based methods. In this study, both pixelbased
and object-based methods were used and results were compared to suggest the
preferred method that may be used in Sundarban. A hybrid object-based classification
method was also developed to simplify the computationally demanding object-based
classification, and to provide a greater flexibility during the classification process in absence
of extensive ground validation data. The relation between NDVI (Normalized Difference
Vegetation Index) and canopy cover was tested in the study area to develop a method to
classify canopy cover type using NDVI value. The classification process was also designed
with three levels of thematic details to see how different thematic scales affect the analysis
results using data of different spatial resolutions. The results show that the classification
accuracy using QuickBird data stays higher than that of Landsat TM data. The difference of
classification accuracy between QuickBird and Landsat TM remains low when thematic
details are low, but becomes progressively pronounced when thematic details are higher.
However, at the highest level of thematic details, the classification was not possible to
conduct due to a lack of appropriate ground validation data.(...)
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Caeiro, Ricardo Alexandre da Silva. "Classificação de dados Landsat 8 do Norte de Portugal com recurso a Geographic Object-Based Image Analysys (GEOBIA)." Master's thesis, 2015. http://hdl.handle.net/10362/17856.
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A deteção remota é uma ciência e técnica, que permite recolher informação de características físicas de um objeto de uma determinada superfície através da radiação eletromagnética, sem entrar em contato com ela. É muito importante na área do planeamento e ordenamento do território e na monitorização da superfície terrestre, ajudando os atores e intervenientes do território no apoio à decisão.
A presente dissertação tem como principal objetivo a classificação de dados Landsat 8 para o Norte de Portugal com recurso a Geographic Object-Based Image Analisys (GEOBIA) e explorar as suas potencialidades e limitações em imagens de baixa resolução espetral, como é o caso das novas imagens Landsat 8, posteriormente, comparou-se o resultado da classificação orientada a objetos com os resultados extraídos da classificação orientada pixel a pixel e segmentos, de modo a avaliar a sua exatidão global e índice de concordância Kappa, nesta dissertação foram usados os software ENVI 5.0 e eCognition 9.0.
A classificação com melhor desempenho de exatidão de global e índice de concordância Kappa é a classificação orientada a objetos, com valores de 56% (14 classes) e 61% (10 classes), e 0,46 (14 classes) e 0,50 (10 classes) respetivamente.
As classificações orientadas a pixel e segmentos obtiveram valores de exatidão global de 49% (14 classes) e 57% (10 classes), 45% (14 classes) e 52% (10 classes), respetivamente. Para o índice de concordância kappa as classificações orientadas a pixel e segmentos obtiveram valores de 0,40 (14 classes) e 0,45 (10 classes), 0,34 (14 classes) e 0,34 (10 classes).
Posteriormente realizou-se uma nova cartografia de ocupação do solo, designada de COS 2015, com o objetivo de descobrir se a classificação orientada a objetos era prejudicada pela incerteza temática da COS 2007 ou pela diferença temporal entre a COS 2007 e a imagem Landsat 8. Posteriormente produziu-se os cálculos de exatidão global e índice de concordância Kappa, Com a exatidão global entre a classificação orientada a objetos e a COS 2007 ser de 59%, com um índice de concordância Kappa de 0,47. Entre a classificação orientada a objetos e a COS 2015, a exatidão global foi de 60% e o índice de concordância Kappa de 0,48.<br>Remote sensing is a science and technique that allows to acquire information about physical features of an object from a particular surface, through electromagnetic radiation without any kind of physical contact with the object itself. This technique is fundamental in the field of planning and land use and also in the monitoring of the Earth’s surface, helping actors and stakeholders in the decision making process. The aim of this thesis is to classify Landsat 8 data in the north of Portugal using Geographic Object-Based Image Analysis (GEOBIA) and explore its potential and limitations in low-resolution spectral images, as in the case of the new Landsat 8 images. Subsequently, the results from the objects-oriented classification was compared to the results extracted from the classicization based on pixel by pixel and segments, in order to assess their overall accuracy and kappa index. The software’s used in this thesis were the ENVI 5.0 and the eCognition 9.0. The classification with better overall accuracy and Kappa index is the object-oriented classification, with values of 56% (14 classes) and 61% (10 classes) and 0.46 (14 classes) and 0.50 (10 classes), respectively. The classifications based on pixels and segments achieved overall accuracy values of 49% (14 classes) and 57% (10 classes), 45% (14 classes) and 52% (10 classes), respectively. For the concordance kappa index, the classifications based on pixels and segments achieved values of 0.40 (14 classes) and 0.45 (10 classes), 0.34 (14 classes) and 0.34 (10 classes). Afterwards, it was carried out a new mapping land cover, designated COS 2015, in order to find out if the classification based on objects was vulnerable due to the thematic uncertainty of the COS 2007 or by the time difference between the COS 2007 and the Landsat 8 image. Finally, the overall accuracy of the classification and the kappa index was calculated. The overall accuracy of the object-oriented classification and the COS 2007 was 59%, with a kappa index of 0.47. In the objects-oriented classification and the COS 2015, the overall accuracy was 60% and the Kappa index was 0.48.
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Jung, Richard. "A multi-sensor approach for land cover classification and monitoring of tidal flats in the German Wadden Sea." Doctoral thesis, 2016. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2016040714380.
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Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring.
The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data.
In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest).
Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster.
Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.
Book chapters on the topic "Pixel-Object classification"
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Küppers, Fabian, Anselm Haselhoff, Jan Kronenberger, and Jonas Schneider. "Confidence Calibration for Object Detection and Segmentation." In Deep Neural Networks and Data for Automated Driving. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01233-4_8.
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AbstractCalibrated confidence estimates obtained from neural networks are crucial, particularly for safety-critical applications such as autonomous driving or medical image diagnosis. However, although the task of confidence calibration has been investigated on classification problems, thorough investigations on object detection and segmentation problems are still missing. Therefore, we focus on the investigation of confidence calibration for object detection and segmentation models in this chapter. We introduce the concept of multivariate confidence calibration that is an extension of well-known calibration methods to the task of object detection and segmentation. This allows for an extended confidence calibration that is also aware of additional features such as bounding box/pixel position and shape information. Furthermore, we extend the expected calibration error (ECE) to measure miscalibration of object detection and segmentation models. We examine several network architectures on MS COCO as well as on Cityscapes and show that especially object detection as well as instance segmentation models are intrinsically miscalibrated given the introduced definition of calibration. Using our proposed calibration methods, we have been able to improve calibration so that it also has a positive impact on the quality of segmentation masks as well.
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Mohd Zaki, Nurul Ain, Intan Nur Suhaida Mohd Radzi, Zulkiflee Abd Latif, Mohd Nazip Suratman, Mohd Zainee Zainal, and Sharifah Norashikin Bohari. "Dominant Tree Species Estimation for Tropical Forest Using Pixel-Based Classification Support Vector Machine (SVM) and Object-Based Classification (OBIA)." In Charting the Sustainable Future of ASEAN in Science and Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3434-8_28.
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Kefi, Chayma, Amina Mabrouk, Nabila Halouani, and Haythem Ismail. "Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas in Coastal Zone." In Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51210-1_336.
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Ali, S. S., P. M. Dare, and S. D. Jones. "A Comparison of Pixel- and Object-Level Data Fusion Using Lidar and High-Resolution Imagery for Enhanced Classification." In Lecture Notes in Geoinformation and Cartography. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93962-7_1.
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Kefi, Chayma, Amina Mabrouk, and Haythem Ismail. "Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas in a Coastal Zone." In Research Developments in Geotechnics, Geo-Informatics and Remote Sensing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72896-0_76.
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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.
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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.
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Sampedro, Carolina, and Carlos F. Mena. "Remote Sensing of Invasive Species in the Galapagos Islands: Comparison of Pixel-Based, Principal Component, and Object-Oriented Image Classification Approaches." In Understanding Invasive Species in the Galapagos Islands. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67177-2_9.
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Sarzana, Tommaso, Antonino Maltese, Alessandra Capolupo, and Eufemia Tarantino. "Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images." In Computational Science and Its Applications – ICCSA 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58811-3_57.
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Kesgin B., Esbah H., and Kurucu Y. "Comparison of pixel-based and object-based classification methods in detecting land use/land cover dynamics." In Remote Sensing for a Changing Europe. IOS Press, 2009. https://doi.org/10.3233/978-1-58603-986-8-173.
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Due to the complex spatial structure of the earth surface, obtaining a detailed and accurate land use/land cover (LULC) classification results with satellite data have still been problematic. The overall goal of this research is to compare the pixel based and object oriented image classification approaches in terms of the overall accuracies and robustness of the final classification product. An Aster image, dated 4/27/2005, with 3 bands from spectral regions of VNIR is used to perform the LULC classification for 16 different LULC classes. Ground truth data are collected from field surveys, available maps and Quickbird images.In pixel-based image analysis, supervised classification is performed by using maximum-likelihood classifier in Erdas 8.7. Object-oriented image analysis is conducted by utilizing Definiens Professional 5.0: The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. During the implementation, several different sets of parameters were tested for image segmentation, 20 was selected as a scale parameter and nearest neighbor was used as the classifier. At the end, the performance of pixel based and object-oriented classifications are compared based on the accuracy assessment results.
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Elatawneh A., Manakos I., Kalaitzidis C., and Schneider T. "Land-Cover Classification and Unmixing of Hyperion Image in Area of Anopoli." In Imagin[e,g] Europe. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-494-8-111.
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The Hyperion sensor is unique because it is the only high spectral resolution hyperspectral sensor on a satellite platform. The hyperspectral capability of the sensor could potentially allow for classifications of increased accuracy, in comparison to those produced from multispectral data. This study evaluates the capability of Hyperion data for discriminating land-cover classes in the Anopoli region (southwest Crete, Greece), through different classification techniques and spectral unmixing procedure. Preprocessing of Hyperion data is essential before any analysis takes place, and it includes interpolation of the wavelengths to a common set, noise reduction by using the minimum noise fraction (MNF), and other commonly applied corrections. Land-cover classifications were performed on the Hyperion satellite data through the Spectral Angle Mapper (SAM) pixel-based technique, as well as the object-based technique. In order to take advantage of the hyperspectral data of Hyperion, and overcome the problem of low spatial resolution, non-linear spectral unmixing approaches were employed, by using artificial neural network (ANN). The spectral end-members used in those approaches were constructed from QuickBird data. All the results were evaluated using the ground truth data derived from high spatial resolution imagery (QuickBird) and field data. All classification results mainly appeared to suffer from the relatively low spatial resolution of the Hyperion sensor and the spectral similarity among many land cover classes. The results show that the object-based classification outperformed the pixel-based classification and achieved the highest overall accuracy. The pixel-based classification and ANN results were highly dependent on the extent that the end-members were representative of the class. However, the pixel-based technique was very successful in differentiating land cover classes with dense vegetation cover.
Conference papers on the topic "Pixel-Object classification"
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Cheng, Yin, Yusen Liao, Xin Sun, and Jun Ke. "Optimizing object classification with single-pixel imaging and transformer networks." In Fourth International Conference on Computational Imaging (CITA 2024), edited by Xiaopeng Shao. SPIE, 2025. https://doi.org/10.1117/12.3057353.
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Jiao, Shuming. "Fast object classification in single-pixel imaging." In Sixth International Conference on Optical and Photonic Engineering, edited by Yingjie Yu, Chao Zuo, and Kemao Qian. SPIE, 2018. http://dx.doi.org/10.1117/12.2502983.
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Wu, Chuang, Lihua Tian, and Chen Li. "Pixel-wise binary classification network for salient object detection." In Eleventh International Conference on Machine Vision, edited by Dmitry P. Nikolaev, Petia Radeva, Antanas Verikas, and Jianhong Zhou. SPIE, 2019. http://dx.doi.org/10.1117/12.2523113.
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Yue, Yuanli, Shouju Liu, and Chao Wang. "Reservoir computing assisted single-pixel high-throughput object classification." In Optical Sensing and Detection VIII, edited by Francis Berghmans and Ioanna Zergioti. SPIE, 2024. http://dx.doi.org/10.1117/12.3022550.
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Raza, Ibad-Ur-Rehman, Syed Saqib Ali Kazmi, Syed Saad Ali, and Ejaz Hussain. "Comparison of Pixel-based and Object-based classification for glacier change detection." In 2012 Second International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2012. http://dx.doi.org/10.1109/eorsa.2012.6261178.
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Cornic, A., K. Ose, D. Ienco, E. Barbe, and R. Cresson. "Assessment of Urban Land-Cover Classification: Comparison Between Pixel and Object Scales." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554617.
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Younis, Mohammed Chachan, Edward Keedwell, and Dragan Savic. "An Investigation of Pixel-Based and Object-Based Image Classification in Remote Sensing." In 2018 International Conference on Advanced Science and Engineering (ICOASE). IEEE, 2018. http://dx.doi.org/10.1109/icoase.2018.8548845.
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Muhammad, Sher, Chaman Gul, Amir Javed, Javeria Muneer, and Mirza Muhammad Waqar. "Comparison of glacier change detection using pixel based and object based classification techniques." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723739.
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Zhang, Meng, and Liang Hong. "Deep Learning Integrated with Multiscale Pixel and Object Features for Hyperspectral Image Classification." In 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS). IEEE, 2018. http://dx.doi.org/10.1109/prrs.2018.8486304.
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Wang, Peifa, Xuezhi Feng, Shuhe Zhao, Pengfeng Xiao, and Chunyan Xu. "Comparison of object-oriented with pixel-based classification techniques on urban classification using TM and IKONOS imagery." In Geoinformatics 2007, edited by Weimin Ju and Shuhe Zhao. SPIE, 2007. http://dx.doi.org/10.1117/12.760759.
Full textReports on the topic "Pixel-Object classification"
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Vick, Tyler. Comparing Pixel- and Object-Based Classification Methods for Determining Land-Cover in the Gee Creek Watershed, Washington. Portland State University Library, 2000. http://dx.doi.org/10.15760/geogmaster.22.
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Bishop, Megan, Vuong Truong, Sophia Bragdon, and Jay Clausen. Comparing the thermal infrared signatures of shallow buried objects and disturbed soil. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49415.
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The alteration of physical and thermal properties of native soil during object burial produces a signature that can be detected using thermal infrared (IR) imagery. This study explores the thermal signature of disturbed soil compared to buried objects of different compositions (e.g., metal and plastic) buried 5 cm below ground surface (bgs) to better understand the mechanisms by which soil disturbance can impact the performance of aided target detection and recognition (AiTD/R). IR imagery recorded every five minutes were coupled with meteorological data recorded on 15-minute intervals from 1 July to 31 October 2022 to compare the diurnal and long-term fluctuations in raw radiance within a 25 × 25 pixel area of interest (AOI) above each target. This study examined the diurnal pattern of the thermal signature under several varying environmental conditions. Results showed that surface effects from soil disturbance increased the raw radiance of the AOI, strengthening the contrast between the object and background soil for several weeks after object burial. Enhancement of the thermal signature may lead to expanded windows of object visibility. Target age was identified as an important element in the development of training data sets for machine learning (ML) classification algorithms.
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Ahn, Yushin, and Richard Poythress. Impervious Surfaces from High Resolution Aerial Imagery: Cities in Fresno County. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2257.
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This study investigates impervious surfaces — areas covered by materials with restricted water permeability, such as pavement, sidewalks, and parking lots—due to their crucial role in influencing water dynamics within urban landscapes. The impermeability of these surfaces disrupts natural water absorption processes, resulting in adverse environmental consequences such as increased flooding, erosion, and water pollution. The research employs impervious surface analysis, a method involving the mapping and analysis of these surfaces within specified study areas, including cities, counties, and census tracts. Remote sensing techniques, specifically satellites and aerial imagery, are commonly utilized for the identification and classification of impervious surfaces. In the context of Fresno County, diverse classification methods, encompassing pixel-based, object-based, and deep learning approaches, are employed to classify and evaluate impervious surfaces. Significantly, the deep learning classification method exhibits exceptional performance, achieving an impressive overall accuracy ranging between 85-92%. The study reveals that the estimated percentage of impervious surfaces in Fresno County cities approximates 45%, comparable to the characteristics of medium density residential areas. Noteworthy is the observation in the Fresno/Clovis city area, where the percentage of impervious surfaces escalated from 53% in 2010 (per EnviroAtlas) to 63% in 2020. This 10% increase over a decade closely aligns with concurrent population growth trends in the region. In conclusion, this research underscores the critical significance of comprehending and monitoring impervious surfaces due to their pivotal role in shaping the environmental quality and resilience of urban areas. The insights gleaned from this study provide valuable guidance for the development of effective land use planning and management strategies, specifically tailored to mitigate the adverse impacts of impervious surfaces on the environment and human well-being.