Log in

Relevant bibliographies by topics / Multi-temporal images / Journal articles

To see the other types of publications on this topic, follow the link: Multi-temporal images.

Journal articles on the topic 'Multi-temporal images'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Multi-temporal images.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Cheng, K. S., C. Wei, and S. C. Chang. "Locating landslides using multi-temporal satellite images." Advances in Space Research 33, no. 3 (2004): 296–301. http://dx.doi.org/10.1016/s0273-1177(03)00471-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

AlMamun, Md, Md Nazrul Islam Mondal, and Boshir Ahmed. "Evaluating Temporal Uncertainty of Multi-temporal Images for Geographical Deviance." International Journal of Computer Applications 103, no. 14 (2014): 14–18. http://dx.doi.org/10.5120/18141-9339.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Wang, Caiqiong, Lei Zhao, Wangfei Zhang, Xiyun Mu, and Shitao Li. "Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning." PeerJ 10 (January 19, 2022): e12805. http://dx.doi.org/10.7717/peerj.12805.

Full text

Abstract:

Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get

APA, Harvard, Vancouver, ISO, and other styles

4

Bu, Lijing, Jiayu Zhang, Zhengpeng Zhang, Yin Yang, and Mingjun Deng. "Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform." Sensors 23, no. 21 (2023): 8916. http://dx.doi.org/10.3390/s23218916.

Full text

Abstract:

The presence of speckle noise severely hampers the interpretability of synthetic aperture radar (SAR) images. While research on despeckling single-temporal SAR images is well-established, there remains a significant gap in the study of despeckling multi-temporal SAR images. Addressing the limitations in the acquisition of the “superimage” and the generation of ratio images within the RABASAR despeckling framework, this paper proposes an enhanced framework. This enhanced framework proposes a direction-based segmentation approach for multi-temporal SAR non-local means filtering (DSMT-NLM) to obt

APA, Harvard, Vancouver, ISO, and other styles

5

Peng, Mingyuan, Canhai Li, Xiaoqing Zhou, and Guoyuan Li. "A Long-time-series Spatio-Temporal-Spectral Fusion Method via Multi-task Learning." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 10, 2024): 567–72. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-567-2024.

Full text

Abstract:

Abstract. Due to the limitations of sensor hardware, clouds and fog, and data transmission limitations, it is difficult for the data obtained by spaceborne remote sensing imager to achieve high temporal, spatial and spectral resolution at the same time, which limits its application in long-time-series high-frequency monitoring. At present, there are several spatio-temporal-spectral algorithms that can realize the fusion of temporal, spatial and spectral resolution, but most of them are based on one to two discrete images, and the integrated fusion at the multi-dimensional level has not yet bee

APA, Harvard, Vancouver, ISO, and other styles

6

Chen, Dan, Xianyun Fei, Jing Li, et al. "Refined Classification of Mountainous Vegetation Based on Multi-Source and Multi-Temporal High-Resolution Images." Forests 16, no. 4 (2025): 707. https://doi.org/10.3390/f16040707.

Full text

Abstract:

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four remote sensing images from different seasons (two aerial images, one WorldView-2 image, and one UAV image) and proposed a vegetation classification method integrating hierarchical extraction and object-oriented approaches for 11 vegetation types. This method innovatively combines the Random F

APA, Harvard, Vancouver, ISO, and other styles

7

Nguyen Hung An. "AN APPROACH FOR IMPROVING ACCURACY OF CHANGE DETECTION IN MULTI-TEMOPRAL SAR IMAGES." Journal of Military Science and Technology, no. 66A (May 6, 2020): 47–54. http://dx.doi.org/10.54939/1859-1043.j.mst.66a.2020.47-54.

Full text

Abstract:

Algorithms of change detection in multi-temporal SAR images have received great interests for recent decades, and been widely applied in natural resource supervision activities. However, these algorithms still expose the limitation of detection accuracy due to inhenrent presence of speckle noise in SAR images. This paper developed a novel approach of change detection in multi-temporal SAR images of sea surface. The algorithm has increased accuracy of change detection in multi-temporal SAR images of sea surface compared with recent other methods.

APA, Harvard, Vancouver, ISO, and other styles

8

Jia, Junhao, Mingzhong Pan, Yaowei Li, et al. "GLTF-Net: Deep-Learning Network for Thick Cloud Removal of Remote Sensing Images via Global–Local Temporality and Features." Remote Sensing 15, no. 21 (2023): 5145. http://dx.doi.org/10.3390/rs15215145.

Full text

Abstract:

Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and th

APA, Harvard, Vancouver, ISO, and other styles

9

Yuhendra and Eva Yulianti. "Multi-Temporal Sentinel-2 Images for Classification Accuracy." Journal of Computer Science 15, no. 2 (2019): 258–68. http://dx.doi.org/10.3844/jcssp.2019.258.268.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Arvind, C. S., Ashoka Vanjare, S. N. Omkar, J. Senthilnath, V. Mani, and P. G. Diwakar. "Flood Assessment using Multi-temporal Modis Satellite Images." Procedia Computer Science 89 (2016): 575–86. http://dx.doi.org/10.1016/j.procs.2016.06.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Tseng, Din-Chang, Hsiao-Ting Tseng, and Chun-Liang Chien. "Automatic cloud removal from multi-temporal SPOT images." Applied Mathematics and Computation 205, no. 2 (2008): 584–600. http://dx.doi.org/10.1016/j.amc.2008.05.050.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Truong, Hoang, Cao, Hayashi, Tadono, and Nasahara. "JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data." Remote Sensing 11, no. 20 (2019): 2412. http://dx.doi.org/10.3390/rs11202412.

Full text

Abstract:

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi

APA, Harvard, Vancouver, ISO, and other styles

13

CLASSIFICATION, AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR MACHINE AND K.-NEAREST NEIGHBOUR ALGORITHMS. "CLASSIFICATION AND COMPARISION OF REMOTE SENSING IMAGE USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOUR ALGORITHMS." Advances in Engineering: an International Journal (ADEIJ) 2, no. 3 (2019): 01–13. https://doi.org/10.5281/zenodo.3237350.

Full text

Abstract:

Remote sensing is collecting information about an object without any direct physical contact with the particular object. It is widely used in many fields such as oceanography, geology, ecology. Remote sensing uses the Satellite to detect and classify the particular object or area. They also classify the object on the earth surfaces which includes Vegetation, Building, Soil, Forest and Water. The approach uses the classifiers of previous images to decrease the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is pre

APA, Harvard, Vancouver, ISO, and other styles

14

Gao, G., M. Zhang, and Y. Gu. "OBJECT MANIFOLD ALIGNMENT FOR MULTI-TEMPORAL HIGH RESOLUTION REMOTE SENSING IMAGES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 325–32. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-325-2017.

Full text

Abstract:

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, “pepper and salt” appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and “pep

APA, Harvard, Vancouver, ISO, and other styles

15

Zhang, Hebing, Hongyi Yuan, Weibing Du, and Xiaoxuan Lyu. "Crop Identification Based on Multi-Temporal Active and Passive Remote Sensing Images." ISPRS International Journal of Geo-Information 11, no. 7 (2022): 388. http://dx.doi.org/10.3390/ijgi11070388.

Full text

Abstract:

Although vegetation index time series from optical images are widely used for crop mapping, it remains difficult to obtain sufficient time-series data because of satellite revisit time and weather in some areas. To address this situation, this paper considered Wen County, Henan Province, Central China as the research area and fused multi-source features such as backscatter coefficient, vegetation index, and time series based on Sentinel-1 and -2 data to identify crops. Through comparative experiments, this paper studied the feasibility of identifying crops with multi-temporal data and fused da

APA, Harvard, Vancouver, ISO, and other styles

16

Li, Qianjing, Jia Tian, and Qingjiu Tian. "Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images." Agriculture 13, no. 4 (2023): 906. http://dx.doi.org/10.3390/agriculture13040906.

Full text

Abstract:

The combination of multi-temporal images and deep learning is an efficient way to obtain accurate crop distributions and so has drawn increasing attention. However, few studies have compared deep learning models with different architectures, so it remains unclear how a deep learning model should be selected for multi-temporal crop classification, and the best possible accuracy is. To address this issue, the present work compares and analyzes a crop classification application based on deep learning models and different time-series data to exploit the possibility of improving crop classification

APA, Harvard, Vancouver, ISO, and other styles

17

Wang, Manlin, Xiaoshuang Ma, Taotao Zheng, and Ziqi Su. "MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images." Sensors 24, no. 21 (2024): 6915. http://dx.doi.org/10.3390/s24216915.

Full text

Abstract:

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents

APA, Harvard, Vancouver, ISO, and other styles

18

Zhang, Peijing, Jinbao Jiang, Peng Kou, Shining Wang, and Bin Wang. "A Multi-Scale Graph Based on Spatio-Temporal-Radiometric Interaction for SAR Image Change Detection." Remote Sensing 16, no. 3 (2024): 560. http://dx.doi.org/10.3390/rs16030560.

Full text

Abstract:

Change detection (CD) in remote sensing imagery has found broad applications in ecosystem service assessment, disaster evaluation, urban planning, land utilization, etc. In this paper, we propose a novel graph model-based method for synthetic aperture radar (SAR) image CD. To mitigate the influence of speckle noise on SAR image CD, we opt for comparing the structures of multi-temporal images instead of the conventional approach of directly comparing pixel values, which is more robust to the speckle noise. Specifically, we first segment the multi-temporal images into square patches at multiple

APA, Harvard, Vancouver, ISO, and other styles

19

Manikandan, Sathianarayanan. "Spatial and Temporal Dynamics of Urban Sprawl Using Multi- temporal Images and Relative Shannon Entropy Model in Adama, Ethiopia." Journal of Advanced Research in Geo Sciences & Remote Sensing 05, no. 3&4 (2019): 48–57. http://dx.doi.org/10.24321/2455.3190.201801.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Liu, Wu Ping, and Fu Wei. "Using Local Transition Probability Models in Markov Random Field for Multi-Temporal Image Classification." Applied Mechanics and Materials 687-691 (November 2014): 3963–67. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3963.

Full text

Abstract:

Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effectiv

APA, Harvard, Vancouver, ISO, and other styles

21

Makuti, S., F. Nex, and M. Y. Yang. "MULTI-TEMPORAL CLASSIFICATION AND CHANGE DETECTION USING UAV IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 651–58. http://dx.doi.org/10.5194/isprs-archives-xlii-2-651-2018.

Full text

Abstract:

In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tes

APA, Harvard, Vancouver, ISO, and other styles

22

Di, Kaichang, Yiliang Liu, Wenmin Hu, Zongyu Yue, and Zhaoqin Liu. "Mars Surface Change Detection from Multi-temporal Orbital Images." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012015. http://dx.doi.org/10.1088/1755-1315/17/1/012015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Islam, Kazi Aminul, Mohammad Shahab Uddin, Chiman Kwan, and Jiang Li. "Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study." Remote Sensing 12, no. 15 (2020): 2455. http://dx.doi.org/10.3390/rs12152455.

Full text

Abstract:

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and mul

APA, Harvard, Vancouver, ISO, and other styles

24

Hessel, C., R. Grompone von Gioi, J. M. Morel, G. Facciolo, P. Arias, and C. de Franchis. "RELATIVE RADIOMETRIC NORMALIZATION USING SEVERAL AUTOMATICALLY CHOSEN REFERENCE IMAGES FOR MULTI-SENSOR, MULTI-TEMPORAL SERIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 845–52. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-845-2020.

Full text

Abstract:

Abstract. We propose a method for the relative radiometric normalization of long, multi-sensor image time series. This allows to increase the revisit time under comparable conditions. Although the relative radiometric normalization is a well-studied problem in the remote sensing community, the availability of an increasing number of images gives rise to new problems. For example, given long series spanning several years, finding features that are maintained through the whole period of time becomes arduous. Instead, we propose in this paper to use automatically detected reference images chosen

APA, Harvard, Vancouver, ISO, and other styles

25

Li, Haoliang, Xingchao Cui, Mingdian Li, Junwu Deng, and Siwei Chen. "Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion." Remote Sensing 15, no. 14 (2023): 3679. http://dx.doi.org/10.3390/rs15143679.

Full text

Abstract:

Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-t

APA, Harvard, Vancouver, ISO, and other styles

26

Meng, Tao, and Mei-Ling Shyu. "Multi-layer Model Collaboration for Bioimage Temporal Stage Classification." International Journal of Semantic Computing 08, no. 02 (2014): 123–44. http://dx.doi.org/10.1142/s1793351x14400017.

Full text

Abstract:

Nowadays, bioimages such as microscopic images and in situ hybridization images increase exponentially. The rapid growth of such images calls for efficient and effective methods for mining significant patterns in them. As a biological process usually consists of several temporal stages, one important task in bioimage analysis is to classify images into different stages. In this paper, a multi-layer model collaboration approach is proposed to capitalize the class correlations in order to enhance the multi-class classification accuracy. First, several middle-level classes, which are relatively e

APA, Harvard, Vancouver, ISO, and other styles

27

Fu, Zhitao, Jian Zhang, and Bo-Hui Tang. "Multi-Temporal Snow-Covered Remote Sensing Image Matching via Image Transformation and Multi-Level Feature Extraction." Optics 5, no. 4 (2024): 392–405. http://dx.doi.org/10.3390/opt5040029.

Full text

Abstract:

To address the challenge of image matching posed by significant modal differences in remote sensing images influenced by snow cover, this paper proposes an innovative image transformation-based matching method. Initially, the Pix2Pix-GAN conversion network is employed to transform remote sensing images with snow cover into images without snow cover, reducing the feature disparity between the images. This conversion facilitates the extraction of more discernible features for matching by transforming the problem from snow-covered to snow-free images. Subsequently, a multi-level feature extractio

APA, Harvard, Vancouver, ISO, and other styles

28

Chung, M., M. Jung, and Y. Kim. "WILDFIRE DAMAGE ASSESSMENT USING MULTI-TEMPORAL SENTINEL-2 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 20, 2019): 97–102. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-97-2019.

Full text

Abstract:

<p><strong>Abstract.</strong> Recently, the drastic climate changes have increased the importance of wildfire monitoring and damage assessment as well as the possibility of wildfire occurrence. Estimation of wildfire damage provides the information on wildfire-induced ecological changes and supports the decision-making process for post-fire treatment activities. For accurate wildfire damage assessment, the discrimination between disaster-induced and natural changes is crucial because they usually coupled together.</p> <p>In this study, Sentinel-2 images were emplo

APA, Harvard, Vancouver, ISO, and other styles

29

Susaki, J., and H. Kishimoto. "IMPROVING THE ACCURACY OF ESTIMATED 3D POSITIONS USING MULTI-TEMPORAL ALOS/PRISM TRIPLET IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 223–30. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-223-2015.

Full text

Abstract:

In this paper, we present a method to improve the accuracy of a digital surface model (DSM) by utilizing multi-temporal triplet images. The Advanced Land Observing Satellite (ALOS) / Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measures triplet images in the forward, nadir, and backward view directions, and a DSM is generated from the obtained set of triplet images. To generate a certain period of DSM, multiple DSMs generated from individual triplet images are compared, and outliers are removed. Our proposed method uses a traditional surveying approach to increase observat

APA, Harvard, Vancouver, ISO, and other styles

30

Lowell, Kim, and Joan Hermann. "Accuracy of Bathymetric Depth Change Maps Using Multi-Temporal Images and Machine Learning." Journal of Marine Science and Engineering 12, no. 8 (2024): 1401. http://dx.doi.org/10.3390/jmse12081401.

Full text

Abstract:

Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirect

APA, Harvard, Vancouver, ISO, and other styles

31

Sharma, Ram C., Hidetake Hirayama, Masatsugu Yasuda, Miki Asai, and Keitarou Hara. "Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images." Journal of Geography and Geology 14, no. 1 (2022): 43. http://dx.doi.org/10.5539/jgg.v14n1p43.

Full text

Abstract:

Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in the previous study for satellite-based classification of plant communities at a broad scale. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites for the classification and mapping of GPE types. This research was conducted in seven representative study sites in different climatic regions ranging from one warm-temperate site in Aya to six

APA, Harvard, Vancouver, ISO, and other styles

32

Saakian, Alexander. "No-till identification by crop residues on the soil surface using the multi-temporal integral index minNDTI." АгроЭкоИнфо 4, no. 46 (2021): 1. http://dx.doi.org/10.51419/20214401.

Full text

Abstract:

The study analyzed the possibility of using the multi-temporal spectral index minNDTI to identify farms using no-till. Using the Google earth engine platform, satellite images of the Sentinel 2 system were obtained, processed and analyzed for two time periods. Based on the data obtained, NDTI images were constructed for periods of fieldwork, as well as multi-temporal minNDTI images. As a result of the statistical analysis, significant differences were found between the NDTI values of the samples from the plowing and no-till options for two time periods in which the field work was carried out a

APA, Harvard, Vancouver, ISO, and other styles

33

Lindsay, Erin, Regula Frauenfelder, Denise Rüther, et al. "Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape." Remote Sensing 14, no. 10 (2022): 2301. http://dx.doi.org/10.3390/rs14102301.

Full text

Abstract:

Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides trigg

APA, Harvard, Vancouver, ISO, and other styles

34

Macelloni, M. M., A. Cina, N. Grasso, and U. Morra di Cella. "MULTI-TEMPORAL AND MULTI-SENSOR GLACIER MONITORING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 165–71. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-165-2023.

Full text

Abstract:

Abstract. Glaciers are subject to drastic mass loss in the last decades and their importance as an environmental and hydrological resource requires regular monitoring. Geomatics techniques are the ideal tool to survey these complex and remote areas, with different remote sensing platforms and sensors to choose from depending on the scale and accuracy to be monitored.In this case study, the Broulè glacier (Valpelline, Aosta Valley, Italy) was monitored for several years using a DJI Phantom 4 RTK UAV, an airborne PhaseOne camera, and a very high-resolution images Pléiades satellite stereo pair t

APA, Harvard, Vancouver, ISO, and other styles

35

Igbokwe, J. I. "Geometrical processing of multi-sensoral multi-temporal satellite images for change detection studies." International Journal of Remote Sensing 20, no. 6 (1999): 1141–48. http://dx.doi.org/10.1080/014311699212902.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Harb, Said, Pedro Achanccaray Diaz, Mehdi Maboudi, and Markus Gerke. "Multi-temporal crack segmentation in concrete structures using deep learning approaches." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 10, 2025): 341–48. https://doi.org/10.5194/isprs-annals-x-g-2025-341-2025.

Full text

Abstract:

Abstract. Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates whether leveraging multi-temporal data for crack segmentation can enhance segmentation quality. Therefore, we compare a Swin UNETR trained on multi-temporal data with a U-Net trained on mono-temporal data to assess the effect of

APA, Harvard, Vancouver, ISO, and other styles

37

Chen, Peng Xiao, Shao Hong Shen, and Xiong Fei Wen. "Remote Sensing Dynamic Monitoring on Illegal Capacity Occupation of Reservoir." Advanced Materials Research 718-720 (July 2013): 1124–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1124.

Full text

Abstract:

Monitoring the illegally occupied channels is very important for the management and regulations of reservoirs. This paper proposes an automatic and efficient approach to identify the changes in the river course with geographic information system and global position system using multi-temporal remote sensing images. Unlike the traditional river course monitoring system, this approach is mainly based on the change detection information extracting from multi-temporal high spatial resolution remote sensing images. Firstly, change detection from different information of multi-temporal remote sensin

APA, Harvard, Vancouver, ISO, and other styles

38

Tang, Junmei, Le Wang, and Zhijun Yao. "Analyzing Urban Sprawl Spatial Fragmentation Using Multi-temporal Satellite Images." GIScience & Remote Sensing 43, no. 3 (2006): 218–32. http://dx.doi.org/10.2747/1548-1603.43.3.218.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Lin, Yunung Nina, Yi-Ching Chen, Yu-Ting Kuo, and Wei-An Chao. "Performance Study of Landslide Detection Using Multi-Temporal SAR Images." Remote Sensing 14, no. 10 (2022): 2444. http://dx.doi.org/10.3390/rs14102444.

Full text

Abstract:

This study addresses one of the most commonly-asked questions in synthetic aperture radar (SAR)-based landslide detection: How the choice of datatypes affects the detection performance. In two examples, the 2018 Hokkaido landslides in Japan and the 2017 Putanpunas landslide in Taiwan, we utilize the Growing Split-Based Approach to obtain Bayesian probability maps for such a performance evaluation. Our result shows that the high-resolution, full-polarimetric data offers superior detection capability for landslides in forest areas, followed by single-polarimetric datasets of high spatial resolut

APA, Harvard, Vancouver, ISO, and other styles

40

Hovsepyan, Azatuhi, Garegin Tepanosyan, Vahagn Muradyan, Shushanik Asmaryan, Andrey Medvedev, and Alexander Koshkarev. "Lake Sevan Shoreline Change Assessment Using Multi-Temporal Landsat Images." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 12, no. 4 (2019): 212–29. http://dx.doi.org/10.24057/2071-9388-2019-46.

Full text

Abstract:

Shoreline changes are important indicators of natural and manmade impacts on inland waters and particularly lakes. Man-induced changes in Lake Sevan water level during the 20th century affected not only the ecological status of the Sevan water but also near-shore areas. This article considers a long-term study of changes in Lake Sevan shoreline that occurred between 1973 and 2015. The Normalized Difference Water Index (NDWI) was applied to delineate the Sevan shoreline changes according to periods of lake water fluctuation from multi-temporal Landsat images and Historical changes in shorelines

APA, Harvard, Vancouver, ISO, and other styles

41

Niu, Ting, Xia Li, Yan Fei Zhou, and Zhao Zhao. "Land Resources Information Acquisition Based on Multi-Temporal TM Images." Advanced Materials Research 610-613 (December 2012): 3636–41. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.3636.

Full text

Abstract:

Selected the lower Tarim River in 2000 and 2010 TM / ETM data source, the use of human - machine interaction visual interpretation of data obtained by two interpretation map, interpret the results based on two data LUCC trends in variation analysis, the results show that: in the past 10 years, the Tarim River increased 28735 ha cultivated land (hm2), the natural increase of 25846 hectares of vegetation (hm2). Forest land was slowly decreasing trend, the decrease in the main area into open woodland and urban settlements; high, shrub and grass cover showed a degree of degradation. Conclusion: Th

APA, Harvard, Vancouver, ISO, and other styles

42

Bhavani, M., V. Hanifar Sangeetha, K. Kalaivani, K. Ulagapriya, and A. Saritha. "Change detection algorithm for multi-temporal satellite images: a review." International Journal of Engineering & Technology 7, no. 2.21 (2018): 206. http://dx.doi.org/10.14419/ijet.v7i2.21.12173.

Full text

Abstract:

Change detection (CD) is the process of detecting changes from multitemporal satellite images that have undergone spatial changes due to natural and man-made disaster. The objective is to analyse different change detection techniques, in order to use appropriately in various applications with the help of image processing. Techniques that are used in current researches are Image Differencing, Image Regression, Change Vector Analysis (CVA),Principal Component Analysis(PCA), Tasselled Cap, Gramm-Schmidt(GS), Post Classification Comparison, EM Detection, Unsupervised Change Detection, Li-Strahler

APA, Harvard, Vancouver, ISO, and other styles

43

Jang, Min-Won, Yi-Hyun Kim, No-Wook Park, and Suk-Young Hong. "Mapping Paddy Rice Varieties Using Multi-temporal RADARSAT SAR Images." Korean Journal of Remote Sensing 28, no. 6 (2012): 653–60. http://dx.doi.org/10.7780/kjrs.2012.28.6.5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Bai, Yang, Ping Tang, and Changmiao Hu. "kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images." Remote Sensing 10, no. 3 (2018): 432. http://dx.doi.org/10.3390/rs10030432.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Han, Youkyung, Byeonghee Kim, Yongil Kim, and Won Hee Lee. "Automatic cloud detection for high spatial resolution multi-temporal images." Remote Sensing Letters 5, no. 7 (2014): 601–8. http://dx.doi.org/10.1080/2150704x.2014.942921.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Cui, Wei, Zhenhong Jia, Xizhong Qin, Jie Yang, and Yingjie Hu. "Multi-temporal Satellite Images Change Detection Algorithm Based on NSCT." Procedia Engineering 24 (2011): 252–56. http://dx.doi.org/10.1016/j.proeng.2011.11.2636.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Yuan, Yuan, Haobo Lv, and Xiaoqiang Lu. "Semi-supervised change detection method for multi-temporal hyperspectral images." Neurocomputing 148 (January 2015): 363–75. http://dx.doi.org/10.1016/j.neucom.2014.06.024.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Canetti, Aline, Marilice Cordeiro Garrastazu, Patrícia Póvoa de Mattos, Evaldo Muñoz Braz, and Sylvio Pellico Netto. "Understanding multi-temporal urban forest cover using high resolution images." Urban Forestry & Urban Greening 29 (January 2018): 106–12. http://dx.doi.org/10.1016/j.ufug.2017.10.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Sahoo, Tapasmini, and Kunal Kumar. "An Approach for Object Detection in Multi Temporal Aerial Images." International Journal of Computer Applications 133, no. 10 (2016): 44–48. http://dx.doi.org/10.5120/ijca2016907961.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Ji, Shunping, Tong Zhang, Qingfeng Guan, and Junli Li. "Nonlinear intensity difference correlation for multi-temporal remote sensing images." International Journal of Applied Earth Observation and Geoinformation 21 (April 2013): 436–43. http://dx.doi.org/10.1016/j.jag.2012.06.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!