12.1.5.5 Semantic Change Detection

Chapter Contents (Back)
Change Detection. Semantic Change Detection.
See also Land Cover Change Analysis, Remote Sensing Change Analysis, Temporal Analysis.

Xiang, S.[Shao], Wang, M.[Mi], Jiang, X.F.[Xiao-Fan], Xie, G.Q.[Guang-Qi], Zhang, Z.Q.[Zhi-Qi], Tang, P.[Peng],
Dual-Task Semantic Change Detection for Remote Sensing Images Using the Generative Change Field Module,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhu, Q.Q.[Qi-Qi], Guo, X.[Xi], Deng, W.H.[Wei-Huan], Shi, S.[Sunan], Guan, Q.F.[Qing-Feng], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei], Li, D.R.[De-Ren],
Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery,
PandRS(184), 2022, pp. 63-78.
Elsevier DOI 2202
Change detection, Semantic change detection, Remote sensing, Imbalanced sample, Siamese network, Change mask BibRef

Shi, S.[Sunan], Zhong, Y.F.[Yan-Fei], Zhao, J.[Ji], Lv, P.Y.[Peng-Yuan], Liu, Y.H.[Yin-He], Zhang, L.P.[Liang-Pei],
Land-Use/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI 2112
Task analysis, Object oriented modeling, Remote sensing, Image segmentation, Spatial resolution, Manuals, Analytical models, remote sensing
See also Multi-Class Geospatial Object Detection Based on a Position-Sensitive Balancing Framework for High Spatial Resolution Remote Sensing Imagery. BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Shu, H., Zhang, L.P.[Liang-Pei],
High-Resolution Image Classification Integrating Spectral-Spatial-Location Cues by Conditional Random Fields,
IP(25), No. 9, September 2016, pp. 4033-4045.
IEEE DOI 1609
geophysical image processing BibRef

Zhang, L.P.[Liang-Pei], Zhao, Y.D.[Yin-Di], Huang, B.[Bo], Li, P.X.[Ping-Xiang],
Texture Feature Fusion with Neighborhood-Oscillating Tabu Search for High Resolution Image Classification,
PhEngRS(74), No. 3, March 2008, pp. 323-332.
DOI Link 0803
Neighborhood-Oscillating tabu search integrates different types of texture features to improve classifi cation performance of high-resolution imagery. BibRef

Zhao, J.[Ji], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Detail-Preserving Smoothing Classifier Based on Conditional Random Fields for High Spatial Resolution Remote Sensing Imagery,
GeoRS(53), No. 5, May 2015, pp. 2440-2452.
IEEE DOI 1502
geophysical image processing BibRef

Zhong, Y.F.[Yan-Fei], Zhao, B.[Bei], Zhang, L.P.[Liang-Pei],
Multiagent Object-Based Classifier for High Spatial Resolution Imagery,
GeoRS(52), No. 2, February 2014, pp. 841-857.
IEEE DOI 1402
evolutionary computation BibRef

Chen, D.Y.[Ding-Yuan], Zhong, Y.F.[Yan-Fei], Ma, A.[Ailong], Zhang, L.P.[Liang-Pei],
Blurry dense object extraction based on buffer parsing network for high-resolution satellite remote sensing imagery,
PandRS(207), 2024, pp. 122-140.
Elsevier DOI Code:
WWW Link. 2401
Blurry dense object extraction, Dense boundary separation, Blurry boundary refinement, Buffer parsing architecture, High-resolution remote sensing imagery BibRef

Zhong, Y.F.[Yan-Fei], Zhao, J., Zhang, L.P.[Liang-Pei],
A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery,
GeoRS(52), No. 11, November 2014, pp. 7023-7037.
IEEE DOI 1407
Context modeling BibRef

Lv, P.Y.[Peng-Yuan], Zhong, Y.F.[Yan-Fei], Zhao, J.[Ji], Zhang, L.P.[Liang-Pei],
Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery,
GeoRS(56), No. 7, July 2018, pp. 4002-4015.
IEEE DOI 1807
geophysical image processing, geophysical techniques, image resolution, image segmentation, remote sensing, HCRF model, remote sensing BibRef

Zheng, Z.[Zhuo], Zhong, Y.F.[Yan-Fei], Tian, S.Q.[Shi-Qi], Ma, A.L.[Ai-Long], Zhang, L.P.[Liang-Pei],
ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection,
PandRS(183), 2022, pp. 228-239.
Elsevier DOI 2201
Multi-task learning, Temporal symmetry, Change detection, Deep learning, Remote sensing, Multi-temporal, Semantic segmentation BibRef

Tian, S.Q.[Shi-Qi], Zhong, Y.F.[Yan-Fei], Zheng, Z.[Zhuo], Ma, A.L.[Ai-Long], Tan, X.C.[Xi-Cheng], Zhang, L.P.[Liang-Pei],
Large-Scale Deep Learning Based Binary and Semantic Change Detection in Ultra High Resolution Remote Sensing Imagery: From Benchmark Datasets to Urban Application,
PandRS(193), 2022, pp. 164-186.
Elsevier DOI 2210
Ultra high resolution, Semantic change detection, Deep learning, Remote sensing BibRef

Tian, S.Q.[Shi-Qi], Tan, X.C.[Xi-Cheng], Ma, A.L.[Ai-Long], Zheng, Z.[Zhuo], Zhang, L.P.[Liang-Pei], Zhong, Y.F.[Yan-Fei],
Temporal-agnostic change region proposal for semantic change detection,
PandRS(204), 2023, pp. 306-320.
Elsevier DOI 2310
Semantic change detection, Deep learning, Attention mechanism, Remote sensing, Salient objects BibRef

Lin, H.[Haihan], Wang, X.Q.[Xiao-Qin], Li, M.M.[Meng-Meng], Huang, D.H.[De-Hua], Wu, R.J.[Rui-Jiao],
A Multi-Task Consistency Enhancement Network for Semantic Change Detection in HR Remote Sensing Images and Application of Non-Agriculturalization,
RS(15), No. 21, 2023, pp. 5106.
DOI Link 2311
BibRef

Ning, X.G.[Xiao-Gang], Zhang, H.C.[Han-Chao], Zhang, R.Q.[Rui-Qian], Huang, X.[Xiao],
Multi-Stage Progressive Change Detection on High Resolution Remote Sensing Imagery,
PandRS(207), 2024, pp. 231-244.
Elsevier DOI 2401
Change detection, Remote sensing images, Stage progressive change detection, Coarse to fine detection, Knowledge distillation BibRef

Zhang, Z.[Zhan], Shu, D.[Daoyu], Liao, C.[Cunyi], Liu, C.Z.[Cheng-Zhi], Zhao, Y.X.[Yuan-Xin], Wang, R.[Ru], Huang, X.[Xiao], Zhang, M.[Mi], Gong, J.Y.[Jian-Ya],
FlexiSAM: A flexible SAM-based semantic segmentation model for land cover classification using high-resolution multimodal remote sensing imagery,
PandRS(227), 2025, pp. 594-612.
Elsevier DOI 2508
Land use and land cover (LULC) classification, Multimodal remote sensing (RS) imagery, Segment anything model (SAM) BibRef

Jiang, L.C.[Liang-Cun], Li, F.[Feng], Huang, L.[Li], Peng, F.F.[Fei-Fei], Hu, L.[Lei],
TTNet: A Temporal-Transform Network for Semantic Change Detection Based on Bi-Temporal Remote Sensing Images,
RS(15), No. 18, 2023, pp. 4555.
DOI Link 2310
BibRef

He, Y.[You], Zhang, H.C.[Han-Chao], 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
BibRef

Tan, L.[Li], Zuo, X.L.[Xiao-Long], Cheng, X.[Xi],
CGMNet: Semantic Change Detection via a Change-Aware Guided Multi-Task Network,
RS(16), No. 13, 2024, pp. 2436.
DOI Link 2407
BibRef

Zou, C.Z.[Chang-Zhong], Wang, Z.Y.[Zi-Yuan],
A semi-parallel CNN-transformer fusion network for semantic change detection,
IVC(149), 2024, pp. 105157.
Elsevier DOI 2408
Fusion semantic change detection network (FSCD), Transformer, Convolutional neural network (CNN), Siamese BibRef

Zhang, F.W.[Feng-Wei], Xia, K.[Kai], Yin, J.X.[Jian-Xin], Deng, S.[Susu], Feng, H.L.[Hai-Lin],
FFPNet: Fine-Grained Feature Perception Network for Semantic Change Detection on Bi-Temporal Remote Sensing Images,
RS(16), No. 21, 2024, pp. 4020.
DOI Link 2411
BibRef

Long, J.[Jiang], Li, M.M.[Meng-Meng], Wang, X.Q.[Xiao-Qin], Stein, A.[Alfred],
Semantic change detection using a hierarchical semantic graph interaction network from high-resolution remote sensing images,
PandRS(211), 2024, pp. 318-335.
Elsevier DOI Code:
WWW Link. 2405
Semantic change detection, Hierarchical semantic graph interaction network, Semantic difference interaction BibRef

Long, J.[Jiang], Liu, S.C.[Si-Cong], Li, M.M.[Meng-Meng], Zhao, H.[Hang], Jin, Y.M.[Yan-Min],
BGSNet: A boundary-guided Siamese multitask network for semantic change detection from high-resolution remote sensing images,
PandRS(225), 2025, pp. 221-237.
Elsevier DOI Code:
WWW Link. 2505
Semantic change detection, Boundary-contextual guided, Multitask network, Uncertainty weighting loss, High-resolution remote sensing images BibRef

Wang, X.[Xin], Liu, S.C.[Si-Cong], Du, P.J.[Pei-Jun], Liang, H.[Hao], Xia, J.S.[Jun-Shi], Li, Y.F.[Yun-Feng],
Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Liu, S.C.[Si-Cong], Du, P.J.[Pei-Jun],
Object-Oriented Change Detection from Multi-Temporal Remotely Sensed Images,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Tang, K.[Kai], Xu, F.[Fei], Chen, X.H.[Xue-Hong], Dong, Q.[Qi], Yuan, Y.H.[Yu-Heng], Chen, J.[Jin],
The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery,
PandRS(211), 2024, pp. 299-317.
Elsevier DOI Code:
WWW Link. 2405
Remote sensing, Change detection, Semantic change detection, Semantic segmentation, Deep learning, Multi-task learning BibRef

Zhou, N.[Ning], Zhou, M.T.[Ming-Ting], Sui, H.G.[Hai-Gang],
DepthCD: Depth prompting in 2D remote sensing imagery change detection,
PandRS(227), 2025, pp. 145-169.
Elsevier DOI 2508
Depth prompt, Dimensional correlation of change, Lightweight adapter, Binary change detection, Semantic change detection BibRef

Xie, Z.[Zhuli], Wan, G.[Gang], Yin, Y.X.[Yun-Xia], Sun, G.[Guangde], Bu, D.D.[Dong-Dong],
SDDGRNets: Level-Level Semantically Decomposed Dynamic Graph Reasoning Network for Remote Sensing Semantic Change Detection,
RS(17), No. 15, 2025, pp. 2641.
DOI Link 2508
BibRef

Li, X.Y.[Xing-Yu], Gong, J.[Jiulu], Wen, J.X.[Jian-Xiong], Wang, Z.P.[Ze-Peng],
MFA-SCDNet: A Semantic Change Detection Network for Visible and Infrared Image Pairs,
RS(17), No. 12, 2025, pp. 2011.
DOI Link 2506
BibRef

Ren, W.Q.[Wei-Qi], Zhang, Z.G.[Zhi-Gang], Liu, S.[Shaowen], Xu, H.R.[Hao-Ran], Ma, Z.[Zheng], Gao, R.[Rui], Kong, Q.M.[Qing-Ming], Dong, S.[Shoutian], Su, Z.B.[Zhong-Bin],
A SHDAViT-MCA Block-Based Network for Remote-Sensing Semantic Change Detection,
RS(17), No. 17, 2025, pp. 3026.
DOI Link 2509
BibRef


Benidir, Y.[Yanis], Gonthier, N.[Nicolas], Mallet, C.[Clément],
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation,
CVPR25(2204-2214)
IEEE DOI Code:
WWW Link. 2508
Training, Earth, Semantics, Pipelines, Land surface, Robustness, inpainting 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

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Misregistration Errors, Evaluation Change Detection .


Last update:Sep 27, 2025 at 16:28:57