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Journal articles on the topic 'Cover Object'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Liu, Wei, Dayu Cheng, Pengcheng Yin, et al. "Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks." ISPRS International Journal of Geo-Information 8, no. 1 (2019): 49. http://dx.doi.org/10.3390/ijgi8010049.

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With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. However, several challenges limit the applications of DCNN in manhole cover object detection using remote sensing imagery: (1) Manhole cover objects often appear at diffe

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Sofyan, Agus. "Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province." AGRIFOR 17, no. 1 (2018): 1. http://dx.doi.org/10.31293/af.v17i1.3090.

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Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) techniq

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Pham, Lan Thi, Son Phi Nguyen, Nghia Viet Nguyen, et al. "Establishment of land cover map using object-oriented classification method for VNREDSat-1 data." Journal of Mining and Earth Sciences 61, no. 2 (2020): 134–44. http://dx.doi.org/10.46326/jmes.2020.61(2).15.

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Land cover/land use classification using high resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values, structures, shapes, and spatial elements. This paper reveals the object-oriented classification method to establish the land cover map using VNREDSat-1 data, with a spatial resolution of 10 m. Land cover/land use system is classified according to CORINE with level 3 with 14 types of land cover/land use. Extraction of 14 types of land cover/land use using object-oriented classification method based on reflectance spectral cha

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Le, Ha Thu Thi, Long Van Hoang, and Trung Van Nguyen. "Object-oriented classification for land cover of North Thang Long Industrial area using Worldview-2 data." Journal of Mining and Earth Sciences 62, no. 1 (2021): 10–18. http://dx.doi.org/10.46326/jmes.2021.62(1).02.

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Land cover/land use classification using high spatial resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values based on structures, shapes, and spatial elements. This paper focuses on the object-oriented classification method to extract artificial surface at industrial area by Worldview-2 data with a spatial resolution of 1.8 m. Extraction of 05 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI index, and de

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He, Jing, Gang Liu, Weile Li, Chuan Tang, and Jiayan Lu. "An evaluation approach for segmentation results of high-resolution remote sensing images based on the degree distribution of land cover networks." International Journal of Modern Physics B 32, no. 25 (2018): 1850283. http://dx.doi.org/10.1142/s0217979218502831.

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Identifying the degree distribution of land cover networks is helpful to find analytical methods for characterizing complex land cover, including segmentation techniques of remote sensing images of land cover. After segmentation, we can obtain the geographical objects and corresponding relationships. In order to evaluate the segmentation results, we introduce the concept of land cover network and present an analysis method based on statistics of its degree distribution. Considering the object-oriented segmentation and objects merge-based spectral difference segmentation, we construct the land

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Ji, X., and X. Niu. "The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (April 23, 2014): 35–40. http://dx.doi.org/10.5194/isprsannals-ii-4-35-2014.

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With the widespread national survey of geographic conditions, object-based data has already became the most common data organization pattern in the area of land cover research. Assessing the accuracy of object-based land cover data is related to lots of processes of data production, such like the efficiency of inside production and the quality of final land cover data. Therefore，there are a great deal of requirements of accuracy assessment of object-based classification map. Traditional approaches for accuracy assessment in surveying and mapping are not aimed at land cover data. It is necessar

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Shelton, Michael B., and Ross L. Goldingay. "Comparative survey techniques for a cryptic Australian snake (Hoplocephalus bitorquatus)." Australian Journal of Zoology 68, no. 2 (2020): 68. http://dx.doi.org/10.1071/zo20062.

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Ecologists endeavour to develop survey techniques that are cost-effective for the species they target and robust enough for statistical analysis. Using time as a measure of effort, we compared visual encounter surveys with artificial cover objects (strapped to trees), targeting an arboreal elapid, the pale-headed snake (Hoplocephalus bitorquatus) and its potential prey (geckos). Within a red gum forest vegetation community with relatively high snake density, capture rates were 0.6 ± 0.1 (s.e.) snakes/person-hour using visual encounter surveys, compared with only 0.1 ± 0.1 snakes/person-hour us

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Mathis, Alicia. "Territoriality in a Terrestrial Salamander: the Influence of Resource Quality and Body Size." Behaviour 112, no. 3-4 (1990): 162–75. http://dx.doi.org/10.1163/156853990x00176.

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AbstractIntraspecific interference competition associated with territoriality has been documented in laboratory studies of the red-backed salamander, Plethodon cinereus. I used laboratory and field experiments to study the effect of resource quality and body size on such competition. In an experiment in southwestern Virginia, cover objects (e.g., logs) from which the resident salamanders were removed were invaded significantly more often than cover objects from which the resident salamander was not removed. These data provide the first direct test of territoriality for a salamander in a natura

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Zhou, Mengyun, and Qingfeng Guan. "A 25-Intersection Model for Representing Topological Relations between Simple Spatial Objects in 3-D Space." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 182. http://dx.doi.org/10.3390/ijgi8040182.

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With the rapid development of the economy, urgent needs for 3-D Geographical Information System (GIS) have sprung up in many application fields. The precise expression of three-dimensional topological relations is the foundation of spatial analysis, topological query, and spatial reasoning in three-dimensional space. In this paper, we subdivide the topological part “boundary” into face, edge, and vertex and propose a 25-intersection model (25IM) to represent topological relations between two simple spatial objects (point, line, region, and body) in 3-D space. An object in the 25IM has five top

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Lv, ZhiYong, TongFei Liu, Jón Atli Benediktsson, Tao Lei, and YiLiang Wan. "Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images." Remote Sensing 10, no. 11 (2018): 1809. http://dx.doi.org/10.3390/rs10111809.

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To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distr

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Ulloa-Torrealba, Yrneh, Reinhold Stahlmann, Martin Wegmann, and Thomas Koellner. "Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany." Remote Sensing 12, no. 24 (2020): 4048. http://dx.doi.org/10.3390/rs12244048.

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The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which c

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Hirata, Yasumasa, and Tomoaki Takahashi. "Image segmentation and classification of Landsat Thematic Mapper data using a sampling approach for forest cover assessmentThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time." Canadian Journal of Forest Research 41, no. 1 (2011): 35–43. http://dx.doi.org/10.1139/x10-130.

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Remote sensing surveys for estimating forest cover may be divided into two approaches: wall-to-wall and sampling. Sampling approaches offer a practical alternative to wall-to-wall mapping, but estimates of forest cover may be affected by the sampling rate of the estimation area. This study aimed to obtain stable estimates of forest cover from satellite data using object-oriented classification at the national level. We investigated a suitable value for the scale parameter in object-oriented classification using eCognition software to identify land cover types, and we evaluated the sampling rat

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Muhammad, Dimyati, Dewanti Dimyati Ratih, Kustiyo, Danoedoro Projo, and Hartono. "Interpretability Evaluation of Annual Mosaic Image of MTB Model for Land Cover Changes Analysis." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 3 (2018): 934–45. https://doi.org/10.12928/TELKOMNIKA.v16i3.9331.

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To verify whether the annual mosaic image of MTB model is acceptable for further digital analysis, it is necessary to evaluate the visual interpretability. The MTB model is an effort to integrate multi-scene and multi-temporal data, to obtain a minimum cloud cover mosaic image in locations that are often covered by clouds and haze. This study is to evaluate the interpretability of the annual mosaic image for analysis of the land cover changes. The data used are the images of 2015, 2016, and 2017 covers a part of central Sumatra. Visual interpretations with a series of steps are used, starting

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Zhang, Chi, Shiqing Wei, Shunping Ji, and Meng Lu. "Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 189. http://dx.doi.org/10.3390/ijgi8040189.

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The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both pol

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Silveira, Eduarda Martiniano de Oliveira, Fausto Weimar Acerbi Júnior, José Márcio de Mello, and Inácio Thomaz Bueno. "Object-based change detection using semivariogram indices derived from NDVI images: The environmental disaster in Mariana, Brazil." Ciência e Agrotecnologia 41, no. 5 (2017): 554–64. http://dx.doi.org/10.1590/1413-70542017415009817.

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ABSTRACT Object-based change detection is a powerful analysis tool for remote sensing data, but few studies consider the potential of temporal semivariogram indices for mapping land-cover changes using object-based approaches. In this study, we explored and evaluated the performance of semivariogram indices calculated from remote sensing imagery, using the Normalized Differential Vegetation Index (NDVI) to detect changes in spatial features related to land cover caused by a disastrous 2015 dam failure in Brazil’s Mariana district. We calculated the NDVI from Landsat 8 images acquired before an

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Li, S. M., Z. Y. Li, E. X. Chen, and Q. W. Liu. "OBJECT-BASED FOREST COVER MONITORING USING GAOFEN-2 HIGH RESOLUTION SATELLITE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (October 17, 2016): 1437–40. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1437-2016.

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Forest cover monitoring is an important part of forest management in local or regional area. The structure and tones of forest can be identified in high spatial remote sensing images. When forests cover change, the spectral characteristics of forests is also changed. In this paper a method on object-based forest cover monitoring with data transformation from time series of high resolution images is put forward. First the NDVI difference image and the composite of PC3,PC4, PC5 of the stacked 8 layers of time series of high resolution satellites are segmented into homogeneous objects. With devel

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Zou, Xiaoliang, Guihua Zhao, Jonathan Li, Yuanxi Yang, and Yong Fang. "OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 733–39. http://dx.doi.org/10.5194/isprs-archives-xli-b3-733-2016.

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With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clou

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Zou, Xiaoliang, Guihua Zhao, Jonathan Li, Yuanxi Yang, and Yong Fang. "OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 733–39. http://dx.doi.org/10.5194/isprsarchives-xli-b3-733-2016.

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With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clou

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19

Yongsheng, Yun, Chang Qingrui, and Xie Jing. "Object-Oriented Land Cover Image Classification System." Recent Patents on Engineering 4, no. 1 (2010): 56–62. http://dx.doi.org/10.2174/187221210790244721.

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Hermawan, Frisa Y., Wadianto ., Ernia Susana, Fauzi Ramadani, and Muhammad Fauzi. "Augmented reality as the medical electronic equipment inventory identifier." International Journal of Scientific Reports 5, no. 11 (2019): 332. http://dx.doi.org/10.18203/issn.2454-2156.intjscirep20194649.

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<p class="abstract"><strong>Background:</strong> Inventory systems are very numerous and require a long time when rechecking. In doing a shortcut time for checking, it can be done the application of augmented reality.</p><p class="abstract"><strong>Methods:</strong> This research can be applied to all types and brands of smartphones. The testing was carried out using three different types and brands of smartphones in three conditions: dark rooms, bright room and open spaces for three times for each smartphone. It examines the suitability of displaying

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Pandie, Cindy Maria Christine, and Ahmad Jum�a Khatib Nur Ali. "Signification functions of Undomestic Goddess novel cover." EduLite: Journal of English Education, Literature and Culture 7, no. 1 (2022): 148. http://dx.doi.org/10.30659/e.7.1.148-159.

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Novel cover is like human appearance; it is the first thing that the people will take notice of. It turned out that novel cover is quite essential in influencing the opinions and impression of the reader regarding the novel itself. Novel�s cover using symbols, fonts, and colors, construct cover signs functions. The researcher intends to analyze the novel covers sign functions. The source data of the research is selected using purposive sampling following the standard of a novel cover. This research uses three (3) covers of the novel Undomestic Goods by Sophie Kinsella as the data source. The t

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Wang, Su-hua, and Elizabeth J. Goldman. "Infants Actively Construct and Update Their Representations of Physical Events: Evidence from Change Detection by 12-Month-Olds." Child Development Research 2016 (November 24, 2016): 1–11. http://dx.doi.org/10.1155/2016/3102481.

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The present research investigates the effects of top-down information on 12-month-olds’ representations of physical events, focusing on their ability to detect an object change across different events. Infants this age typically fail to detect height changes in events with tubes even though they successfully do so in events with covers. In Experiment 1, infants who saw a tube event in which objects did not interact successfully detected a change in an object’s height, suggesting that object interaction affects infants’ categorization of physical events. Experiments 2 and 3 examined the fine-gr

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Dobson, Chris. "Relativistic acceleration in noninertial frames of a line of objects." American Journal of Physics 92, no. 4 (2024): 258–69. http://dx.doi.org/10.1119/5.0144523.

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A general solution to the problem of relativistic acceleration of point objects in the noninertial frame of any of the objects is given in one spatial dimension. The objects are initially at rest in a common inertial frame and accelerate until they are at rest in a second inertial frame. The starting time and position of each object, the acceleration rate of each object, and the number of objects are arbitrary. The solution gives the position and velocity of each object in the noninertial frame of the host object, and the proper time of each, as functions of the proper time of the host. The me

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Hadavand, A., M. Saadatseresht, and S. Homayouni. "A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 263–68. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-263-2015.

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In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimiz

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Hasegawa, Hiroaki, Yosuke Suzuki, Aiguo Ming, Masatoshi Ishikawa, and Makoto Shimojo. "Robot Hand Whose Fingertip Covered with Net-Shape Proximity Sensor - Moving Object Tracking Using Proximity Sensing -." Journal of Robotics and Mechatronics 23, no. 3 (2011): 328–37. http://dx.doi.org/10.20965/jrm.2011.p0328.

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Occlusion in several millimeters from an object to be grasped made it difficult for a vision-sensor-based approach to detect relative positioning between this object and robot fingers joint grasping. The proximity sensor we proposed detects the object at a near range very effectively. We developed a thin proximity sensor sheet to cover the 3 fingers of a robot hand. Integrating sensors and hand control, we implemented an objecttracking controller. Using proximity sensory signals, the controller coordinates wrist positioning based on palm proximity sensors and grasping from fingertip sensors, e

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Commandre, B., D. En-Nejjary, L. Pibre, M. Chaumont, C. Delenne, and N. Chahinian. "MANHOLE COVER LOCALIZATION IN AERIAL IMAGES WITH A DEEP LEARNING APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 333–38. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-333-2017.

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Urban growth is an ongoing trend and one of its direct consequences is the development of buried utility networks. Locating these networks is becoming a challenging task. While the labeling of large objects in aerial images is extensively studied in Geosciences, the localization of small objects (smaller than a building) is in counter part less studied and very challenging due to the variance of object colors, cluttered neighborhood, non-uniform background, shadows and aspect ratios. In this paper, we put forward a method for the automatic detection and localization of manhole covers in Very H

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Crivei, Septimiu. "On Flat Objects of Finitely Accessible Categories." Scientific World Journal 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/451091.

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Flat objects of a finitely accessible additive category are described in terms of some objects of the associated functor category of , called strongly flat functors. We study closure properties of the class of strongly flat functors, and we use them to deduce the known result that every object of a finitely accessible abelian category has a flat cover.

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Shi, Hao, Jiahe Fan, Yupei Wang, and Liang Chen. "Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images." Remote Sensing 13, no. 18 (2021): 3715. http://dx.doi.org/10.3390/rs13183715.

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Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images. In recent years, convolutional network (CNN)-based land cover classification methods have achieved great advancement. However, previous methods fail to generate fine segmentation results, especially for the object boundary pixels. In order to obtain boundary-preserving predictions, we first propose to incorporate spatially adapting contextual cues. In this way, objects with similar appearance can be eff

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Liu, B., S. Du, and X. Zhang. "LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 39–43. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-39-2020.

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Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization usin

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Antonarakis, A. S., K. S. Richards, and J. Brasington. "Object-based land cover classification using airborne LiDAR." Remote Sensing of Environment 112, no. 6 (2008): 2988–98. http://dx.doi.org/10.1016/j.rse.2008.02.004.

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Soiland-Reyes, Stian, Carole Goble, and Paul Groth. "Evaluating FAIR Digital Object and Linked Data as distributed object systems." PeerJ Computer Science 10 (April 30, 2024): e1781. http://dx.doi.org/10.7717/peerj-cs.1781.

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FAIR Digital Object (FDO) is an emerging concept that is highlighted by European Open Science Cloud (EOSC) as a potential candidate for building an ecosystem of machine-actionable research outputs. In this work we systematically evaluate FDO and its implementations as a global distributed object system, by using five different conceptual frameworks that cover interoperability, middleware, FAIR principles, EOSC requirements and FDO guidelines themself. We compare the FDO approach with established Linked Data practices and the existing Web architecture, and provide a brief history of the Semanti

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Jaber, Hussein Sabah, Muntadher Aidi Shareef, and Zainab Fahkri Merzah. "OBJECT-BASED APPROACHES FOR LAND USE-LAND COVER CLASSIFICATION USING HIGH RESOLUTION QUICK BIRD SATELLITE IMAGERY (A CASE STUDY: KERBELA, IRAQ)." Geodesy and cartography 48, no. 2 (2022): 85–91. http://dx.doi.org/10.3846/gac.2022.14453.

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Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood

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Zhang, Xin, Liangxiu Han, Lianghao Han, and Liang Zhu. "How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?" Remote Sensing 12, no. 3 (2020): 417. http://dx.doi.org/10.3390/rs12030417.

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Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown p

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Zou, Xiaoliang, Guihua Zhao, Jonathan Li, Yuanxi Yang, and Yong Fang. "3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 741–47. http://dx.doi.org/10.5194/isprs-archives-xli-b1-741-2016.

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Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrar

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Zou, Xiaoliang, Guihua Zhao, Jonathan Li, Yuanxi Yang, and Yong Fang. "3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 741–47. http://dx.doi.org/10.5194/isprsarchives-xli-b1-741-2016.

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Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrar

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Gubeladze, O. A., and A. R. Gubeladze. "Substantiating the Need to Cover Atomic Energy Objects from Air Strikes." Global Nuclear Safety 45, no. 4 (2022): 15–25. http://dx.doi.org/10.26583/gns-2022-04-02.

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The article deals with one of the nuclear terrorism problem aspects, namely the attempts to implement “nuclear” blackmail by individual states. The possible destructive aircrafts impact to objects using atomic energy is considered. An express assessment of the guided air bomb strike result on the reinforced concrete object structure was carried out.

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Agnes Aryasanti, Ratna Ujiandari, Mardi Hardjianto, and Ratna Kusumawardani. "Implementasi Keamanan File Menggunakan Metode Kriptografi Base-64 dan Steganografi Least Significant Bit (LSB) Random 2-Bit Berbasis Web." Jurnal Ticom: Technology of Information and Communication 11, no. 2 (2023): 113–18. http://dx.doi.org/10.70309/ticom.v11i2.78.

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Keamanan dan kerahasiaan adalah dua aspek penting dari transmisi data dalam saluran komunikasi. Dalam pengiriman yang bersifat data rahasia, pengirim menginginkan data tersebut terkirim ke tujuan tanpa ada orang lain yang mengetahuinya. Salah satu cara agar data rahasia agar tidak dapat dibaca oleh orang yang tidak berwenang adalah menggunakan steganografi. Teknik steganografi menyembunyikan data rahasia ke dalam suatu obyek (cover object). Media yang digunakan untuk cover object adalah citra digital dengan kedalaman warna 24-bit. Metode steganografi yang digunakan untuk mengamankan data adala

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Sang, H., L. Zhai, J. Zhang та F. An. "An object-oriented approach for agrivultural land classiﬁcation using rapideye imagery". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (26 червня 2015): 145–48. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-145-2015.

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With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential

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KOZIOŁ, Krystian, Jerzy CZERNIEC, Kamil MACIUK, Michał JANKOWSKI, Celso A. G. SANTOS, and Paulina LEWIŃSKA. "CASE STUDY OF ON-THE-SPOT AND SURFACE MEDIEVAL OBJECTS - VERIFYING CURRENT REMOTE METHODS OF DOCUMENTING ARCHAEOLOGICAL SITES." International Journal of Conservation Science 14, no. 1 (2023): 201–16. http://dx.doi.org/10.36868/ijcs.2023.01.13.

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One of the limitations of satellite techniques in archaeological investigations is the land cover. Heritage objects may be more or less visible depending on the land cover type. Low vegetation, such as grass or small shrubs, will usually allow us to determine the location of archaeological objects if the difference in height between the object and vegetation is not significant enough. Forest or high vegetation cover makes it more challenging to determine the location of potentially artificial structures. This project's study area covered 50×70 km of the lower reaches of the Vistula River, Pola

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Piao, Zhengquan, Junbo Wang, Linbo Tang, Baojun Zhao, and Shichao Zhou. "Anchor-Free Object Detection with Scale-Aware Networks for Autonomous Driving." Electronics 11, no. 20 (2022): 3303. http://dx.doi.org/10.3390/electronics11203303.

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Current anchor-free object detectors do not rely on anchors and obtain comparable accuracy with anchor-based detectors. However, anchor-free object detectors that adopt a single-level feature map and lack a feature pyramid network (FPN) prior information about an object’s scale; thus, they insufficiently adapt to large object scale variation, especially for autonomous driving in complex road scenes. To address this problem, we propose a divide-and-conquer solution and attempt to introduce some prior information about object scale variation into the model when maintaining a streamlined network

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Joshi, Abhasha, Janak Raj Joshi, Nawaraj Shrestha, Saroj Shrestha, and Sudarshan Gautam. "Object Based Land Cover Extraction Using Open Source Software." Journal on Geoinformatics, Nepal 12 (October 31, 2013): 26–30. http://dx.doi.org/10.3126/njg.v12i0.9070.

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Land cover is observed bio-physical cover of the earth’s surface and is an important resource for global monitoring studies, resource management, and planning activities. Traditionally these land resources were obtained from imagery using pixel based image analysis. But with the advent of High resolution satellite imagery and computation techniques these data are now widely being prepared using Object based Image Analysis (OBIA) techniques. But mostly only algorithm provided in commercial software and Ecognition in particular is being used to study OBIA. This paper aims to assess the applicati

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Sari, Nurwita Mustika, and Dony Kushardono. "KLASIFIKASI PENUTUP LAHAN BERBASIS OBJEK PADA DATA FOTO UAV UNTUK MENDUKUNG PENYEDIAAN INFORMASI PENGINDERAAN JAUH SKALA RINCI." Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital 11, no. 2 (2014): 114–27. https://doi.org/10.30536/inderaja.v11i2.3304.

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The need of spatial information from detailed-scale remote sensing is increasing. Unmanned Aerial Vehicle or UAV become one of vehicles that is expected to obtain such information. Production of land cover spatial information using UAV photo data requires appropriate method for classification. This study proposes an object-based classification method for land cover based on Haralick texture information namely homogeneity, contrast, dissimilarity, entropy, angular second moment, mean, standard deviation, and correlation. As a comparison method, a conventional land cover-object-based classificat

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Ahmed, Faisal, Waheed Noor, Mohammad Atif Nasim, Ihsan Ullah, and Abdul Basit. "Vegetation and Non-Vegetation Classification Using Object Detection Techniques and Deep Learning from Low/Mixed Resolution Satellite Images." Pakistan Journal of Emerging Science and Technologies (PJEST) 4, no. 4 (2023): 1–18. http://dx.doi.org/10.58619/pjest.v4i4.152.

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Vegetation cover classification using mixed or low-resolution scalar images is challenging. Fortunately, recently deep learning object detection methods have emerged as a replacement to the conventional machine learning methods for the detection and classification of land use and land cover. This paper presents a deep learning object detection approach for land use and land cover detection using low/mixed resolution satellite images acquired from Google Earth satellite images. Google Earth images are accessible freely using the Google Earth Pro desktop application. Our dataset consists of two

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Fikri, Muhammad Alfin, and F. X. Ferdinandus. "OPTIMASI TEKNIK STEGANOGRAFI AMELSBR PADA EMPAT BIT TERAKHIR DENGAN COVER IMAGE BERWARNA." Antivirus : Jurnal Ilmiah Teknik Informatika 16, no. 1 (2022): 25–38. http://dx.doi.org/10.35457/antivirus.v16i1.1967.

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Pertumbuhan transmisi data yang dinamis di era modern membutuhkan pertukaran data yang aman. Biasanya, digunakan steganografi atau kriptografi. Steganografi adalah teknik yang robust untuk melindungi data tersembunyi dari akses tidak sah (unauthorized access) dengan memasukkannya ke dalam cover object, tanpa mengubah kualitas dari cover object. Tujuan: Penelitian ini mengusulkan optimasi teknik steganografi Adaptive Minimum Error Least Significant Bit Replacement (AMELSBR) dengan memodifikasi setidaknya empat bit yang akan diganti. Sayangnya, steganografi seringkali terdeteksi oleh mata manusi

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Phiri, Darius, Matamyo Simwanda, Vincent Nyirenda, Yuji Murayama, and Manjula Ranagalage. "Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification." ISPRS International Journal of Geo-Information 9, no. 5 (2020): 329. http://dx.doi.org/10.3390/ijgi9050329.

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Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Cop

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Wang, Zhihao, Yalan Liu, Yuhuan Ren, and Haojie Ma. "Object-Level Double Constrained Method for Land Cover Change Detection." Sensors 19, no. 1 (2018): 79. http://dx.doi.org/10.3390/s19010079.

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Land cover change detection based on remote sensing has become increasingly important for protecting the ecological environment. Spatial features of images can be extracted by object-level methods. However, the computational complexity is high when using many features to detect land cover change. Meanwhile, single-constrained change detection (SCCD) methods produce non-objective and inaccurate results. Therefore, we proposed a land cover change detection method: the object-level double constrained change detection (ODCD) method. First, spectral and spatial features were calculated based on mul

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Gui, Shengxi, Shuang Song, Rongjun Qin, and Yang Tang. "Remote Sensing Object Detection in the Deep Learning Era—A Review." Remote Sensing 16, no. 2 (2024): 327. http://dx.doi.org/10.3390/rs16020327.

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Given the large volume of remote sensing images collected daily, automatic object detection and segmentation have been a consistent need in Earth observation (EO). However, objects of interest vary in shape, size, appearance, and reflecting properties. This is not only reflected by the fact that these objects exhibit differences due to their geographical diversity but also by the fact that these objects appear differently in images collected from different sensors (optical and radar) and platforms (satellite, aerial, and unmanned aerial vehicles (UAV)). Although there exists a plethora of obje

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Ikhsani, Hanifah, Muhammad Ikhwan, and Ika Lestari. "ANALISIS TINGGI TERBANG UNMANNED AERIAL VEHICLE TERHADAP KLASIFIKASI TUTUPAN LAHAN MENGGUNAKAN OBJECT BASED IMAGE ANALYSIS." ZONAsi: Jurnal Sistem Informasi 6, no. 1 (2024): 38–47. http://dx.doi.org/10.31849/zn.v6i1.14983.

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The development of forest and land cover classification methods was currently increasing rapidly, one of which is by utilizing remote sensing applications, namely UAV technology. One method with high accuracy that can be used to classify forest and land cover is the object-based image analysis (OBIA) classification method. There is an assumption that the results of orthophoto shooting will look the same at a glance, but each orthophoto result also has a different spatial resolution and affects the detail of each OBIA segmentation. This makes this research important to do how to classify forest

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Islam, Mirajul, Nushrat Jahan Ria, and Jannatul Ferdous Ani. "Satellite Imageries for Detection of Bangladesh’s Rural and Urban Areas Using YOLOv5 and CNN." Mobile Information Systems 2023 (July 17, 2023): 1–11. http://dx.doi.org/10.1155/2023/1814906.

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In recent years, there have been significant advancements in object identification in natural photos. However, when applying natural image object recognition techniques directly to satellite images, the results are often unsatisfactory. This is primarily due to inherent disparities in the object scale and orientation caused by the omniscient viewpoint of satellite imagery. The distinguishing factors between rural and urban areas lie in the objects that cover them. Furthermore, the complex backdrop of satellite photos poses challenges in accurately extracting features, leading to the omission o

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Ahmed, Muhammad, Khurram Azeem Hashmi, Alain Pagani, Marcus Liwicki, Didier Stricker, and Muhammad Zeshan Afzal. "Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments." Sensors 21, no. 15 (2021): 5116. http://dx.doi.org/10.3390/s21155116.

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Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable pro

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