23.2.9 Land Cover Change Analysis, Remote Sensing Change Analysis, Temporal Analysis

Chapter Contents (Back)
Classification. Change Detection. Land Cover. Temporal Analysis. Remote Sensing. Agricultural.
See also Land Cover Change Analysis Using Learning, Neural Nets. Image level, non specific:
See also Change Detection for Remote Sensing Image Level.
See also Land Use Change Analysis.
See also Changes using Landsat Images. SAR and Radar:
See also Land Cover, Land Use Change Analysis for Radar and SAR. Urban specific:
See also Change Detection, Urban Area Land Cover, Temporal Analysis. Applications to specific regions:
See also Applied Change Analysis, Specific Site Applications, Site Specific Temporal. Longer term changes:
See also Land Cover Change Analysis, Seasonal, Annual Variations, Climate Change, Analysis. Global:
See also Land Cover Change Analysis, Global Changes, Global Analysis. Image level:
See also Change Detection -- Image Level.
See also Change Detection for Damage Assessment.
See also Night Time Image Analysis for Urban Area Detection, Change and Growth. Forest changes:
See also Forest Change Evaluation, Change Detection, Temporal Analysis.
See also Rice Crop Analysis, Production, Detection, Health, Change.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

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DOI Link 2101
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PandRS(177), 2021, pp. 75-88.
Elsevier DOI 2106
Self-organizing map, Class noise reduction, Bayesian inference, Satellite image time series, Land use and cover classification BibRef

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DOI Link 2107
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Simple Nonlinear Iterative Temporal Clustering,
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IEEE DOI 2109
Image segmentation, Satellites, Time series analysis, Feature extraction, Spatiotemporal phenomena, time series BibRef

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Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification,
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Deep Siamese Networks Based Change Detection with Remote Sensing Images,
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Ke, Q.T.[Qing-Tian], Zhang, P.[Peng],
MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection,
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DOI Link 2109
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Li, W.S.[Wei-Sheng], Cao, D.W.[Dong-Wen], Peng, Y.D.[Yi-Dong], Yang, C.[Chao],
MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution,
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DOI Link 2109
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Li, W.S.[Wei-Sheng], Cao, D.W.[Dong-Wen], Xiang, M.H.[Ming-Hao],
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RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Li, W.S.[Wei-Sheng], Wu, F.Y.[Feng-Yan], Cao, D.W.[Dong-Wen],
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RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Zhang, Z.[Zheng], Tang, P.[Ping], Zhang, W.X.[Wei-Xiong], Tang, L.[Liang],
Satellite Image Time Series Clustering via Time Adaptive Optimal Transport,
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DOI Link 2110
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Attention to Both Global and Local Features: A Novel Temporal Encoder for Satellite Image Time Series Classification,
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Guo, W.Q.[Wen-Qi], Zhang, W.X.[Wei-Xiong], Zhang, Z.[Zheng], Tang, P.[Ping], Gao, S.C.[Shi-Chen],
Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis,
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Elsevier DOI 2112
Change detection, Eagle eye, Synthetic boat sequence, Synthetic dataset, Unmanned aerial vehicle BibRef

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Wang, Y.M.[Yi-Ming], Zhang, Z.X.[Zeng-Xin], Chen, X.[Xi],
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Zhao, J.Q.[Jian-Qiao], Cao, Y.[Yue], Yu, L.[Le],
Global Change of Land-Sparing and Land-Sharing Patterns over the Past 30 Years: Evidence from Remote Sensing and Statistics,
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DOI Link 2112
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Lopez-Fornieles, E.[Eva], Brunel, G.[Guilhem], Rancon, F.[Florian], Gaci, B.[Belal], Metz, M.[Maxime], Devaux, N.[Nicolas], Taylor, J.[James], Tisseyre, B.[Bruno], Roger, J.M.[Jean-Michel],
Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
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Tang, J.J.[Jia-Jia], Liang, J.[Jie], Yang, Y.J.[Yong-Jun], Zhang, S.L.[Shao-Liang], Hou, H.[Huping], Zhu, X.X.[Xiao-Xiao],
Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Ha, T.[Thuan], Shen, Y.B.[Yan-Ben], Duddu, H.[Hema], Johnson, E.[Eric], Shirtliffe, S.J.[Steven J.],
Quantifying Hail Damage in Crops Using Sentinel-2 Imagery,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
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Ke, Q.T.[Qing-Tian], Zhang, P.[Peng],
Hybrid-TransCD: A Hybrid Transformer Remote Sensing Image Change Detection Network via Token Aggregation,
IJGI(11), No. 4, 2022, pp. xx-yy.
DOI Link 2205
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Li, J.[Jun], Qin, T.T.[Ting-Ting], Zhang, C.Y.[Cheng-Ye], Zheng, H.Y.[Hui-Yu], Guo, J.T.[Jun-Ting], Xie, H.Z.[Hui-Zhen], Zhang, C.Y.[Cai-Yue], Zhang, Y.C.[Yi-Cong],
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A Continuous Change Tracker Model for Remote Sensing Time Series Reconstruction,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Nyborg, J.[Joachim], Pelletier, C.[Charlotte], Lefèvre, S.[Sébastien], Assent, I.[Ira],
TimeMatch: Unsupervised cross-region adaptation by temporal shift estimation,
PandRS(188), 2022, pp. 301-313.
Elsevier DOI 2205
Satellite image time series, Temporal shift, Crop classification, Domain adaptation, Deep learning BibRef

Moncrieff, G.R.[Glenn R.],
Continuous Land Cover Change Detection in a Critically Endangered Shrubland Ecosystem Using Neural Networks,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Guerrisi, G.[Giorgia], del Frate, F.[Fabio], Schiavon, G.[Giovanni],
Satellite On-Board Change Detection via Auto-Associative Neural Networks,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Zhang, Z.[Zheng], Tang, P.[Ping], Hu, C.M.[Chang-Miao], Liu, Z.Q.[Zhi-Qiang], Zhang, W.X.[Wei-Xiong], Tang, L.[Liang],
Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Jing, W.P.[Wei-Peng], Zhu, S.Y.[Song-Yu], Kang, P.L.[Pei-Lun], Wang, J.[Jian], Cui, S.J.[Sheng-Jia], Chen, G.S.[Guang-Sheng], Song, H.[Houbing],
Remote Sensing Change Detection Based on Unsupervised Multi-Attention Slow Feature Analysis,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
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Zhang, H.B.[He-Bing], Yuan, H.Y.[Hong-Yi], Du, W.B.[Wei-Bing], Lyu, X.X.[Xiao-Xuan],
Crop Identification Based on Multi-Temporal Active and Passive Remote Sensing Images,
IJGI(11), No. 7, 2022, pp. xx-yy.
DOI Link 2208
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Fu, W.Q.[Wei-Qi], Shao, P.[Pan], Dong, T.[Ting], Liu, Z.W.[Zhe-Wei],
Novel Higher-Order Clique Conditional Random Field to Unsupervised Change Detection for Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Radoi, A.[Anamaria],
Multimodal Satellite Image Time Series Analysis Using GAN-Based Domain Translation and Matrix Profile,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
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Zhang, Y.Q.[Yu-Qi], Li, W.[Wei], Wang, Y.H.[Yao-Hua], Wang, Z.B.[Zhi-Bin], Li, H.[Hao],
Beyond Classifiers: Remote Sensing Change Detection with Metric Learning,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Tian, J.[Juan], Peng, D.[Daifeng], Guan, H.Y.[Hai-Yan], Ding, H.Y.[Hai-Yong],
RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Zhou, Y.[Yong], Wang, F.K.[Feng-Kai], Zhao, J.Q.[Jia-Qi], Yao, R.[Rui], Chen, S.[Silin], Ma, H.P.[He-Ping],
Spatial-Temporal Based Multihead Self-Attention for Remote Sensing Image Change Detection,
CirSysVideo(32), No. 10, October 2022, pp. 6615-6626.
IEEE DOI 2210
Feature extraction, Remote sensing, Task analysis, Imaging, Transformers, Interference, Building change detection, attention mechanism BibRef

Li, B.[Bin], Wang, G.H.[Guang-Hui], Zhang, T.[Tao], Yang, H.[Huachao], Zhang, S.[Shubi],
Remote Sensing Image-Change Detection with Pre-Generation of Depthwise-Separable Change-Salient Maps,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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van Duynhoven, A.[Alysha], Dragicevic, S.[Suzana],
Assessing the Impact of Neighborhood Size on Temporal Convolutional Networks for Modeling Land Cover Change,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Wang, Y.D.[Yi-Dan], Zhou, X.W.[Xue-Wen], Ao, Z.R.[Zu-Rui], Xiao, K.[Kun], Yan, C.X.[Chen-Xi], Xin, Q.C.[Qin-Chuan],
Gap-Filling and Missing Information Recovery for Time Series of MODIS Data Using Deep Learning-Based Methods,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Li, J.L.[Jin-Long], Yuan, X.C.[Xiao-Chen], Li, J.F.[Jin-Feng], Huang, G.H.[Guo-Heng], Li, P.[Ping], Feng, L.[Li],
CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Xiong, S.[Sitian], Baltezar, P.[Priscilla], Crowley, M.A.[Morgan A.], Cecil, M.[Michael], Crema, S.C.[Stefano C.], Baldwin, E.[Eli], Cardille, J.A.[Jeffrey A.], Estes, L.[Lyndon],
Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Feng, S.[Shou], Fan, Y.Z.[Yuan-Ze], Tang, Y.J.[Ying-Jie], Cheng, H.[Hao], Zhao, C.H.[Chun-Hui], Zhu, Y.[Yaoxuan], Cheng, C.H.[Chun-Hua],
A Change Detection Method Based on Multi-Scale Adaptive Convolution Kernel Network and Multimodal Conditional Random Field for Multi-Temporal Multispectral Images,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Mao, Z.[Zan], Tong, X.Y.[Xin-Yu], Luo, Z.[Ze], Zhang, H.H.[Hong-Hai],
MFATNet: Multi-Scale Feature Aggregation via Transformer for Remote Sensing Image Change Detection,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Patra, R.K.[Raj Kumar], Patil, S.N.[Sujata N.], Falkowski-Gilski, P.[Przemyslaw], Lubniewski, Z.[Zbigniew], Poongodan, R.[Rachana],
Feature Weighted Attention: Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Adil, E.[Elyar], Yang, X.L.[Xiang-Li], Huang, P.P.[Ping-Ping], Liu, X.L.[Xiao-Long], Tan, W.X.[Wei-Xian], Yang, J.X.[Jian-Xi],
Cascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
Change. BibRef

Shrestha, M.[Megha], Mitra, C.[Chandana], Rahman, M.[Mahjabin], Marzen, L.[Luke],
Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
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Lv, Z.Y.[Zhi-Yong], Huang, H.T.[Hai-Tao], Li, X.H.[Xing-Hua], Zhao, M.H.[Ming-Hua], Benediktsson, J.A.[Jón Atli], Sun, W.[WeiWei], Falco, N.[Nicola],
Land Cover Change Detection With Heterogeneous Remote Sensing Images: Review, Progress, and Perspective,
PIEEE(110), No. 12, December 2022, pp. 1976-1991.
IEEE DOI 2301
Remote sensing, Optical sensors, Optical imaging, Terrain factors, Heterogeneous networks, Earthquakes, Adaptive optics, multiresolution change detection BibRef

Xing, H.Q.[Hua-Qiao], Wang, H.H.[Hai-Hang], Zhang, J.H.[Jin-Hua], Hou, D.Y.[Dong-Yang],
Monitoring Land Cover Change by Leveraging a Dynamic Service-Oriented Computing Model,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Yuan, S.Y.[Shi-Ying], Zhong, R.F.[Ruo-Fei], Li, Q.Y.[Qing-Yang], Dong, Y.X.[Ya-Xin],
MFGFNet: A Multi-Scale Remote Sensing Change Detection Network Using the Global Filter in the Frequency Domain,
RS(15), No. 6, 2023, pp. 1682.
DOI Link 2304
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Mohammadi, S.[Sina], Belgiu, M.[Mariana], Stein, A.[Alfred],
Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks,
PandRS(198), 2023, pp. 272-283.
Elsevier DOI 2304
Crop mapping, Deep learning, Fully convolutional neural networks, Time series BibRef

Zhang, Q.[Qi], Lu, Y.[Yao], Shao, S.C.[Si-Cheng], Shen, L.[Li], Wang, F.[Fei], Zhang, X.T.[Xue-Tao],
MFNet: Mutual Feature-Aware Networks for Remote Sensing Change Detection,
RS(15), No. 8, 2023, pp. 2145.
DOI Link 2305
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Tian, L.W.[Ling-Wen], Meng, Y.Y.[Yuan-Yuan], Zhu, L.H.[Li-Hong], Zou, X.Y.[Xin-Yu], Liu, X.N.[Xiang-Nan],
Graph-based spatial pattern multi-type change detection,
PandRS(199), 2023, pp. 258-271.
Elsevier DOI 2305
Spatial pattern, Graph-based, Change detection, Time series, Spatial relationship BibRef

Cardama, F.J.[F. Javier], Heras, D.B.[Dora B.], Argüello, F.[Francisco],
Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images,
RS(15), No. 11, 2023, pp. 2889.
DOI Link 2306
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Shahbandeh, M.[Mahsa], Kaim, D.[Dominik], Kozak, J.[Jacek],
Using CORONA Imagery to Study Land Use and Land Cover Change: A Review of Applications,
RS(15), No. 11, 2023, pp. 2793.
DOI Link 2306
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Liu, X.L.[Xiao-Le], Wang, G.J.[Guang-Jun], Shi, Y.[Yu], Liang, S.[Sihai], Jia, J.Z.[Jin-Zhang],
Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Xu, M.Z.[Ming-Zhu], Shang, R.[Rong], Chen, J.M.[Jing M.], Zeng, L.F.[Ling-Fang],
LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters,
RS(15), No. 13, 2023, pp. 3277.
DOI Link 2307
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Li, K.Y.[Kai-Yuan], Zhao, W.Z.[Wen-Zhi], Chen, J.[Jiage], Zhang, L.Q.[Li-Qiang], Hu, D.D.[Duo-Duo], Wang, Q.[Qiao],
Predicting Crop Growth Patterns with Spatial-Temporal Deep Feature Exploration for Early Mapping,
RS(15), No. 13, 2023, pp. 3285.
DOI Link 2307
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Cheng, X.L.[Xing-Lu], Sun, Y.H.[Yong-Hua], Zhang, W.K.[Wang-Kuan], Wang, Y.H.[Yi-Han], Cao, X.Y.[Xu-Yue], Wang, Y.Z.[Yan-Zhao],
Application of Deep Learning in Multitemporal Remote Sensing Image Classification,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308
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He, Y.[You], Zhang, H.[Hanchao], Ning, X.G.[Xiao-Gang], Zhang, R.Q.[Rui-Qian], Chang, D.[Dong], Hao, M.H.[Ming-Hui],
Spatial-Temporal Semantic Perception Network for Remote Sensing Image Semantic Change Detection,
RS(15), No. 16, 2023, pp. 4095.
DOI Link 2309
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Suwanlee, S.R.[Savittri Ratanopad], Keawsomsee, S.[Surasak], Pengjunsang, M.[Morakot], Homtong, N.[Nudthawud], Prakobya, A.[Amornchai], Borgogno-Mondino, E.[Enrico], Sarvia, F.[Filippo], Somard, J.[Jaturong],
Monitoring Agricultural Land and Land Cover Change from 2001-2021 of the Chi River Basin, Thailand Using Multi-Temporal Landsat Data Based on Google Earth Engine,
RS(15), No. 17, 2023, pp. 4339.
DOI Link 2310
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Chang, S.Z.[Shi-Zhen], Ghamisi, P.[Pedram],
Changes to Captions: An Attentive Network for Remote Sensing Change Captioning,
IP(32), 2023, pp. 6047-6060.
IEEE DOI Code:
WWW Link. 2311
BibRef

Chen, M.[Min], Zhang, Q.[Qiangjiang], Ge, X.M.[Xu-Ming], Xu, B.[Bo], Hu, H.[Han], Zhu, Q.[Qing], Zhang, X.[Xin],
A Full-Scale Connected CNN-Transformer Network for Remote Sensing Image Change Detection,
RS(15), No. 22, 2023, pp. 5383.
DOI Link 2311
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Chen, H.R.X.[Hong-Rui-Xuan], Song, J.[Jian], Wu, C.[Chen], Du, B.[Bo], Yokoya, N.[Naoto],
Exchange means change: An unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange,
PandRS(206), 2023, pp. 87-105.
Elsevier DOI 2312
Single-temporal change detection, Image patch exchange, Adaptive clustering, Deep learning, Convolutional neural network BibRef

Maiwald, F.[Ferdinand], Feurer, D.[Denis], Eltner, A.[Anette],
Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images,
PandRS(206), 2023, pp. 184-200.
Elsevier DOI Code:
WWW Link. 2312
Historical aerial images, Feature matching, Neural networks, Structure-from-motion, Digital surface model, Multi-temporal BibRef

Cai, C.[Chen], Wang, Y.[Yi], Yap, K.H.[Kim-Hui],
Interactive Change-Aware Transformer Network for Remote Sensing Image Change Captioning,
RS(15), No. 23, 2023, pp. 5611.
DOI Link 2312
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Matyukira, C.[Charles], Mhangara, P.[Paidamwoyo],
Land Cover and Landscape Structural Changes Using Extreme Gradient Boosting Random Forest and Fragmentation Analysis,
RS(15), No. 23, 2023, pp. 5520.
DOI Link 2312
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Zhu, D.[Daoye], Han, B.[Bing], Silva, E.A.[Elisabete A.], Li, S.[Shuang], Huang, M.[Min], Ren, F.[Fuhu], Cheng, C.Q.[Cheng-Qi],
Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples,
RS(15), No. 23, 2023, pp. 5528.
DOI Link 2312
BibRef

Ma, H.L.[Hang-Ling], Zhao, L.R.[Ling-Ran], Li, B.Q.[Bing-Quan], Niu, R.Q.[Rui-Qing], Wang, Y.Y.[Yue-Yue],
Change Detection Needs Neighborhood Interaction in Transformer,
RS(15), No. 23, 2023, pp. 5459.
DOI Link 2312
BibRef

Qu, F.[Fang], Sun, Y.Q.[You-Qiang], Zhou, M.[Man], Liu, L.[Liu], Yang, H.M.[Hua-Min], Zhang, J.Q.[Jun-Qing], Huang, H.[He], Hong, D.F.[Dan-Feng],
Vegetation Land Segmentation with Multi-Modal and Multi-Temporal Remote Sensing Images: A Temporal Learning Approach and a New Dataset,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
multi-temporal; multi-modal; Vegetation Knowledge Graph (VKG); VRS-Sys; VRSFormer BibRef

Racic, M.[Matej], Oštir, K.[Krištof], Zupanc, A.[Anže], Zajc, L.C.[Luka Cehovin],
Multi-Year Time Series Transfer Learning: Application of Early Crop Classification,
RS(16), No. 2, 2024, pp. 270.
DOI Link 2402
BibRef

Wang, Y.K.[Yu-Kun], Wang, M.M.[Meng-Meng], Hao, Z.[Zhonghu], Wang, Q.[Qiang], Wang, Q.W.[Qian-Wen], Ye, Y.Y.X.[Yuan-Yan-Xin],
MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection,
RS(16), No. 3, 2024, pp. 572.
DOI Link 2402
BibRef

Cao, B.[Bo], Wang, Y.[Yan], Zhang, X.L.[Xiao-Long], Shen, Y.J.[Yan-Jun],
Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin,
RS(16), No. 3, 2024, pp. 587.
DOI Link 2402
BibRef

Wang, L.K.[Lu-Kang], Zhang, M.[Min], Gao, X.[Xu], Shi, W.Z.[Wen-Zhong],
Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms,
RS(16), No. 5, 2024, pp. 804.
DOI Link 2403
BibRef

Zhou, M.[Meng], Qian, W.X.[Wei-Xian], Ren, K.[Kan],
Multistage Interaction Network for Remote Sensing Change Detection,
RS(16), No. 6, 2024, pp. 1077.
DOI Link 2403
BibRef

Kumawat, M.[Manisha], Khaparde, A.[Arti],
Time-Variant Satellite Vegetation Classification Enabled by Hybrid Metaheuristic-Based Adaptive Time-Weighted Dynamic Time Warping,
IJIG(24), No. 2, March 2024, pp. 2450016.
DOI Link 2404
BibRef

Oubara, A.[Amel], Wu, F.[Falin], Maleki, R.[Reza], Ma, B.[Boyi], Amamra, A.[Abdenour], Yang, G.[Gongliu],
Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism,
IJGI(13), No. 4, 2024, pp. 125.
DOI Link 2405
BibRef

Dubrovin, K.[Konstantin], Verkhoturov, A.[Andrey], Stepanov, A.[Alexey], Aseeva, T.[Tatiana],
Multi-Year Cropland Mapping Based on Remote Sensing Data: A Case Study for the Khabarovsk Territory, Russia,
RS(16), No. 9, 2024, pp. 1633.
DOI Link 2405
BibRef

Che, X.[Xianghong], Zhang, H.K.[Hankui K.], Li, Z.B.B.[Zhong-Bin B.], Wang, Y.[Yong], Sun, Q.[Qing], Luo, D.[Dong], Wang, H.[Hao],
Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks,
PandRS(212), 2024, pp. 73-95.
Elsevier DOI 2406
Satellite image time series, Linear interpolation, Missing values, Landsat and Sentinel-2, Transformer BibRef

Lin, L.J.[Lu-Jun], Liu, L.[Lei], Liu, M.[Ming], Zhang, Q.J.[Qun-Jia], Feng, M.[Min], Khalil, Y.S.[Yasir Shaheen], Yin, F.[Fang],
DEDNet: Dual-Encoder DeeplabV3+ Network for Rock Glacier Recognition Based on Multispectral Remote Sensing Image,
RS(16), No. 14, 2024, pp. 2603.
DOI Link 2408
BibRef

Zheng, Z.[Zhuo], Tian, S.Q.[Shi-Qi], Ma, A.[Ailong], Zhang, L.P.[Liang-Pei], Zhong, Y.F.[Yan-Fei],
Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process,
ICCV23(21761-21770)
IEEE DOI 2401
BibRef

Arja, S.[Sami], Marcireau, A.[Alexandre], Balthazor, R.L.[Richard L.], McHarg, M.G.[Matthew G.], Afshar, S.[Saeed], Cohen, G.[Gregory],
Density Invariant Contrast Maximization for Neuromorphic Earth Observations,
EventVision23(3984-3994)
IEEE DOI 2309
BibRef

Diaw, M.[Moustapha], Landré, J.[Jérôme], Delahaies, A.[Agnès], Morain-Nicolier, F.[Frédéric], Retraint, F.[Florent],
Satellite Image Change Detection Using Disjoint Information and Local Dissimilarity Map,
ICIP22(36-40)
IEEE DOI 2211
Deep learning, Satellites, Change detection, Disjoint Information, Local Dissimilarity Map, Weibull threshold BibRef

Toker, A.[Aysim], Kondmann, L.[Lukas], Weber, M.[Mark], Eisenberger, M.[Marvin], Camero, A.[Andrés], Hu, J.L.[Jing-Liang], Hoderlein, A.P.[Ariadna Pregel], Senaras, Ç.[Çaglar], Davis, T.[Timothy], Cremers, D.[Daniel], Marchisio, G.[Giovanni], Zhu, X.X.[Xiao Xiang], Leal-Taixé, L.[Laura],
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation,
CVPR22(21126-21135)
IEEE DOI 2210
Image segmentation, Satellites, Protocols, Annotations, Semantics, Training data, Semisupervised learning, Datasets and evaluation BibRef

Zheng, Z.[Zhuo], Ma, A.L.[Ai-Long], Zhang, L.P.[Liang-Pei], Zhong, Y.F.[Yan-Fei],
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery,
ICCV21(15173-15182)
IEEE DOI 2203
Image segmentation, Supervised learning, Semantics, Detectors, Solids, Representation learning BibRef

Olayinka, D.N., Omolaye, K.L., Ilesanmi, A.J., Okolie, C.J., Arungwa, I.D.,
Application of UAV Surveys for Evaluating the Productivity Levels of Traditional and Mechanised Farmers in a Customary Land Tenure System,
ISPRS21(B3-2021: 617-622).
DOI Link 2201
BibRef

Qadeer, M.U.[Muhammad Usman], Saeed, S.[Salar], Taj, M.[Murtaza], Muhammad, A.[Abubakr],
Spatio-Temporal Crop Classification on Volumetric Data,
ICIP21(3812-3816)
IEEE DOI 2201
Solid modeling, Satellites, Image processing, Benchmark testing, Developing countries, Satellite data, CNN, Crop Classification BibRef

Verma, S.[Sagar], Panigrahi, A.[Akash], Gupta, S.[Siddharth],
QFabric: Multi-Task Change Detection Dataset,
EarthVision21(1052-1061)
IEEE DOI 2109
Dataset, Change Detection. Deep learning, Urban areas, Predictive models, Benchmark testing, Metadata, Pattern recognition BibRef

Tuna, C., Merciol, F.[François], Lefèvre, S.[Sébastien],
Spatio-Temporal Object Stability for Monitoring Evolving Areas In Satellite Image Time Series,
ISPRS20(B2:1273-1280).
DOI Link 2012
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Karakizi, C., Tsiotas, I.A., Kandylakis, Z., Vaiopoulos, A., Karantzalos, K.,
Assessing the Contribution of Spectral and Temporal Features for Annual Land Cover and Crop Type Mapping,
ISPRS20(B3:1555-1562).
DOI Link 2012
BibRef

Molinari, M.E., Monti-Guarnieri, A., Manzoni, M.,
A Novel Index for Temporal Stability Analysis In Space and Time Of SAR-derived Scenes,
ISPRS20(B3:1577-1583).
DOI Link 2012
BibRef

Mondal, M.S., Sharma, N., Kappas, M., Garg, P.K.,
Cellular Automata (ca) Contiguity Filters Impacts on CA Markov Modeling Of Land Use Land Cover Change Predictions Results,
ISPRS20(B3:1585-1591).
DOI Link 2012
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Belgiu, M., Zhou, Y., Marshall, M., Stein, A.,
Dynamic Time Warping for Crops Mapping,
ISPRS20(B3:947-951).
DOI Link 2012
BibRef

Rimba, A.B., Atmaja, T., Mohan, G., Chapagain, S.K., Arumansawang, A., Payus, C., Fukushi, K.,
Identifying Land Use and Land Cover (LULC) Change From 2000 to 2025 Driven By Tourism Growth: A Study Case In Bali,
ISPRS20(B3:1621-1627).
DOI Link 2012
BibRef

Nguyen, H.T.T., Pham, T.A., Doan, M.T., Tran, P.T.X.,
Land Use/land Cover Change Prediction Using Multi-temporal Satellite Imagery and Multi-layer Perceptron Markov Model,
Gi4DM20(99-105).
DOI Link 2012
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Nasirzadehdizaji, R., Sanli, F.B., Cakir, Z.,
Application of Sentinel-1 Multi-temporal Data for Crop Monitoring And Mapping,
SMPR19(803-807).
DOI Link 1912
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Najafi, Z., Fatehi, P., Darvishsefat, A.A.,
Vegetation Dynamics Trend Using Satellite Time Series Imagery,
SMPR19(783-788).
DOI Link 1912
BibRef

Kerdegari, H.[Hamideh], Razaak, M.[Manzoor], Argyriou, V.[Vasileios], Remagnino, P.[Paolo],
Smart Monitoring of Crops Using Generative Adversarial Networks,
CAIP19(I:554-563).
Springer DOI 1909
BibRef

Ouyang, S., Fan, K., Wang, H., Wang, Z.,
Change Detection of Remote Sensing Images By DT-CWT and MRF,
Hannover17(3-10).
DOI Link 1805
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Dumitru, C.O., Schwarz, G., Datcu, M.,
Machine Learning Techniques for Knowledge Extraction From Satellite Images: Application to Specific Area Types,
ISPRS21(B3-2021: 455-462).
DOI Link 2201
BibRef
And:
Image representation alternatives for the analysis of satellite image time series,
MultiTemp17(1-4)
IEEE DOI 1712
data analysis, feature extraction, geophysical image processing, geophysical techniques, image representation, semantics BibRef

Kukawska, E., Lewinski, S., Krupinski, M., Malinowski, R., Nowakowski, A., Rybicki, M., Kotarba, A.,
Multitemporal Sentinel-2 data: remarks and observations,
MultiTemp17(1-4)
IEEE DOI 1712
land cover, ESA, European Space Agency, Sentinel-2 system, data end users, land cover data base, Time series analysis BibRef

Pelletier, C., Valero, S., Inglada, J., Dedieu, G., Champion, N.,
Filtering mislabeled data for improving time series classification,
MultiTemp17(1-4)
IEEE DOI 1712
vegetation, accurate land cover maps, filtering mislabeled data, geographical area, improving time series classification, Vegetation BibRef

Luppino, L.T.[Luigi Tommaso], Anfinsen, S.N.[Stian Normann], Moser, G.[Gabriele], Jenssen, R.[Robert], Bianchi, F.M.[Filippo Maria], Serpico, S.[Sebastiano], Mercier, G.[Gregoire],
A Clustering Approach to Heterogeneous Change Detection,
SCIA17(II: 181-192).
Springer DOI 1706
heterogeneous multitemporal satellite images. BibRef

Diaz, P.M.A., Feitosa, R.Q., Sanches, I.D., Costa, G.A.O.P.,
A Method To Estimate Temporal Interaction In A Conditional Random Field Based Approach For Crop Recognition,
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Ding, Y.L.[Yu-Lin], Lin, H.[Hui], Li, R.R.[Rong-Rong],
Change Semantic Constrained Online Data Cleaning Method For Real-time Observational Data Stream,
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DOI Link 1610
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Xie, Z.L.[Zhen-Lei], Shi, R.M.[Ruo-Ming], Zhu, L.[Ling], Peng, S.[Shu], Chen, X.[Xu],
Comparison Of Pixel-based And Object-oriented Land Cover Change Detection Methods,
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DOI Link 1610
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Zhang, H.P.[Hao-Peng], Jiang, Z.G.[Zhi-Guo], Cheng, Y.[Yan],
Land Cover Change Detection Using Saliency and Wavelet Transformation,
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Colomo-Jiménez, C., Pérez-García, J.L., Fernández-del Castillo, T., Gómez-López, J.M., Mozas-Calvache, A.T.,
Methodology For Orientation And Fusion Of Photogrammetric And Lidar Datas For Multitemporal Studies,
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Areshkina, L.V., Belazerskii, L.A., Oreshkin, N.,
The Automation of the Process of Land Area Change Detection in Permanent Monitoring Systems,
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Moller, M., Gerstmann, H., Thurkow, D., Gao, F.[Feng], Forster, M.,
Coupling of phenological information and synthetically generated time-series for crop types as indicator for vegetation coverage information,
MultiTemp15(1-4)
IEEE DOI 1511
crops BibRef

Gressin, A.[Adrien], Vincent, N.[Nicole], Mallet, C.[Clement], Paparoditis, N.[Nicolas],
A unified framework for land-cover database update and enrichment using satellite imagery,
ICIP14(5057-5061)
IEEE DOI 1502
Accuracy BibRef

Iwaniak, A., Lukowicz, J., Strzelecki, M., Kaczmarek, I.,
Ontology Driven Analysis of Spatio-temporal Phenomena, Aimed At Spatial Planning And Environmental Forecasting,
SSG13(119-124).
DOI Link 1402
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Hyun, C.U., Lee, J.S., Lee, I.,
Assessment of hydrogen fluoride damage to vegetation using optical remote sensing data,
SSG13(115-118).
DOI Link 1402
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Kesikoglu, M.H., Atasever, Ü.H., Özkan, C.,
Unsupervised change detection in satellite images using fuzzy c-means clustering and principal component analysis,
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Sasagawa, A., Baltsavias, E., Kocaman Aksakal, S., Wegner, J.D.,
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Vahidi, H., Monabbati, E.,
Contextual Image Classification Approach for Monitoring of Agricultural Land Cover by Support Vector Machines and Markov Random Fields,
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Luzi, G., Crosetto, M., Devanthéry, N., Cuevas, M., Meng, X.,
Change Detection and Dynamic Analysis Based on Remote Sensing Images,
IWIDF13(185-188).
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Choi, G.M.[Gveong Min], Junz, H.I.[Hvun Il], Kim, R.K.[Rae Kwang], Oh, W.G.[Weon Geun],
Image modeling system development for robust descriptor of environmental change,
FCV13(64-66).
IEEE DOI 1304
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Madhava Rao, V., Hermon, R.R., Kesava Rao, P., Phanindra Kumar, T.,
Impact Assessment of Watershed In Desert Region,
ISPRS12(XXXIX-B8:327-331).
DOI Link 1209
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Braun, A.C., Weidner, U., Hinz, S.,
Kernel-composition For Change Detection In Medium Resolution Remote Sensing Data,
ISPRS12(XXXIX-B7:281-286).
DOI Link 1209
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Wang, J., Koizumi, H., Kamiya, T.,
Accuracy Improvement of Change Detection Based on Color Analysis,
ISPRS12(XXXIX-B7:357-361).
DOI Link 1209
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Artese, G., Achilli, V., Fabris, M., Perrelli, M.,
A Semiautomatic Anomalous Change Detection Method For Monitoring Aims,
ISPRS12(XXXIX-B7:263-268).
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Šafár, V., Ždímal, V.,
Identification Of Land Cover In The Past Using Infrared Images At Present,
ISPRS12(XXXIX-B7:229-234).
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Salleh, S.A., Abd Latif, Z., Wan Mohd, W.M.N., Chan, A.,
Albedo Pattern Recognition And Time-series Analyses In Malaysia,
ISPRS12(XXXIX-B7:235-240).
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Waldhoff, G., Curdt, C., Hoffmeister, D., Bareth, G.,
Analysis of Multitemporal and Multisensor Remote Sensing Data for Crop Rotation Mapping,
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Zhu, B.[Bo], Gong, W.[Wei], Shi, S.[Shuo], Song, S.L.[Sha-Lei], Ma, Y.Y.[Ying-Ying],
A New Effective Way On Vegetation Mornitoring Using Multi-spectral Canopy Lidar,
ISPRS12(XXXIX-B1:83-85).
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Rodrigues, A.[Arlete], Marcal, A.R.S.[Andre R. S.], Cunha, M.[Mario],
PhenoSat: A tool for vegetation temporal analysis from satellite image data,
MultiTemp11(45-48).
IEEE DOI 1109
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Small, C.[Christopher],
Spatiotemporal dimensionality and time-space characterization of vegetation phenology from multitemporal MODIS EVI,
MultiTemp11(65-68).
IEEE DOI 1109
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Aiazzi, B., Alparone, L., Baronti, S., Carla, R., Garzelli, A., Santurri, L., Selva, M.,
Effects of multitemporal scene changes on pansharpening fusion,
MultiTemp11(73-76).
IEEE DOI 1109
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Ruiz, L.A., Recio, J.A., Hermosilla, T., Fernández-Sarría, A.,
Identification of Agricultural and Land Cover Database Changes Using Object-oriented Classification Techniques,
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Masse, A.[Antoine], Ducrot, D.[Danielle], Marthon, P.[Philippe],
Tools for multitemporal analysis and classification of multisource satellite imagery,
MultiTemp11(209-212).
IEEE DOI 1109
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Caccetta, P., Collings, S., Hingee, K., McFarlane, D., Wu, X.L.[Xiao-Liang],
Fine-Scale Monitoring of Complex Environments Using Remotely Sensed Aerial, Satellite, and Other Spatial Data,
ISIDF11(1-5).
IEEE DOI 1111
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Cui, S.Y.[Shi-Yong], Datcu, M.,
Mining Satellite Image Time Series: Statistical Modeling and Evolution Analysis,
ISIDF11(1-4).
IEEE DOI 1111
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Ma, Y.[Yuan], Li, H.T.[Hai-Tao], Gu, H.Y.[Hai-Yan],
A Study of Fast Change Detection Algorithm Based on Feature Library of Remote Sensing Imagery,
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IEEE DOI 1111
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Li, S.[Shuang], Xie, Y.C.[Yi-Chun], Meng, L.K.[Ling-Kui],
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MultiTemp11(29-32).
IEEE DOI 1109
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He, F.Q.[Fen-Qin], Yin, J.Z.[Jian-Zhong],
Research on CA Differencing for Remote Sensing Change Detection,
CISP09(1-4).
IEEE DOI 0910
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Parulekar, R., Davis, L.S., Chellappa, R., Saltz, J., Sussman, A., Townshend, J.,
High performance computing for land cover dynamics,
ICPR94(C:234-238).
IEEE DOI 9410
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Very High Resolution Land Cover Change Analysis .

Last update:Aug 28, 2024 at 16:02:19