Nielsen, A.A.,
The Regularized Iteratively Reweighted MAD Method for Change Detection
in Multi- and Hyperspectral Data,
IP(16), No. 2, February 2007, pp. 463-478.
IEEE DOI
0702
BibRef
Nielsen, A.A.,
Conradsen, K.,
Andersen, O.B.,
Change Detection in the 1996-1997 AVHRR Oceans Pathfinder Sea Surface
Temperature Data,
SCIA01(O-Tu4A).
0206
BibRef
Eismann, M.T.,
Meola, J.,
Hardie, R.C.,
Hyperspectral Change Detection in the Presence of Diurnal and Seasonal
Variations,
GeoRS(46), No. 1, January 2008, pp. 237-249.
IEEE DOI
0712
BibRef
Meola, J.,
Eismann, M.T.,
Moses, R.L.,
Ash, J.N.,
Detecting Changes in Hyperspectral Imagery Using a Model-Based Approach,
GeoRS(49), No. 7, July 2011, pp. 2647-2661.
IEEE DOI
1107
BibRef
Meola, J.,
Eismann, M.T.,
Moses, R.L.,
Ash, J.N.,
Application of Model-Based Change Detection to Airborne VNIR/SWIR
Hyperspectral Imagery,
GeoRS(50), No. 10, October 2012, pp. 3693-3706.
IEEE DOI
1210
BibRef
Eismann, M.T.,
Stocker, A.D.,
Nasrabadi, N.M.,
Automated Hyperspectral Cueing for Civilian Search and Rescue,
PIEEE(97), No. 6, June 2009, pp. 1031-1055.
IEEE DOI
0905
BibRef
Liu, S.C.[Si-Cong],
Bruzzone, L.,
Bovolo, F.,
Zanetti, M.,
Du, P.J.[Pei-Jun],
Sequential Spectral Change Vector Analysis for Iteratively
Discovering and Detecting Multiple Changes in Hyperspectral Images,
GeoRS(53), No. 8, August 2015, pp. 4363-4378.
IEEE DOI
1506
geophysical image processing
BibRef
Zanetti, M.,
Bovolo, F.,
Bruzzone, L.,
Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for
Change Detection in Multispectral Images,
IP(24), No. 12, December 2015, pp. 5004-5016.
IEEE DOI
1512
Gaussian distribution
BibRef
Zanetti, M.[Massimo],
Bruzzone, L.,
Piecewise Linear Approximation of Vector-Valued Images and Curves via
Second-Order Variational Model,
IP(26), No. 9, September 2017, pp. 4414-4429.
IEEE DOI
1708
approximation theory, gradient methods, image colour analysis,
image restoration, minimisation, vectors, BZ model,
Blake-Zisserman model, RGB imagery, bandwise processing,
first-order model, free gradient discontinuity,
image restoration-regularization problem,
BibRef
Liu, S.C.[Si-Cong],
Bruzzone, L.,
Bovolo, F.,
Du, P.J.[Pei-Jun],
Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple
Changes in Hyperspectral Images,
GeoRS(54), No. 5, May 2016, pp. 2733-2748.
IEEE DOI
1604
hyperspectral imaging
BibRef
Ye, Y.,
Bruzzone, L.,
Shan, J.,
Bovolo, F.,
Zhu, Q.,
Fast and Robust Matching for Multimodal Remote Sensing Image
Registration,
GeoRS(57), No. 11, November 2019, pp. 9059-9070.
IEEE DOI
1911
Feature extraction, Remote sensing, Image matching, Histograms,
Frequency-domain analysis, Shape, Image registration,
pixelwise feature representation
BibRef
Wang, Q.[Qi],
Yuan, Z.H.[Zheng-Hang],
Du, Q.[Qian],
Li, X.L.[Xue-Long],
GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral
Image Change Detection,
GeoRS(57), No. 1, January 2019, pp. 3-13.
IEEE DOI
1901
Hyperspectral imaging, Machine learning, Task analysis,
Neural networks, Principal component analysis,
spectral unmixing
BibRef
Li, X.L.[Xue-Long],
Yuan, Z.H.[Zheng-Hang],
Wang, Q.[Qi],
Unsupervised Deep Noise Modeling for Hyperspectral Image Change
Detection,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Guo, Q.L.[Qing-Le],
Zhang, J.P.[Jun-Ping],
Zhang, Y.[Ye],
Multitemporal Images Change Detection Based on AMMF and Spectral
Constraint Strategy,
GeoRS(59), No. 4, April 2021, pp. 3444-3457.
IEEE DOI
2104
Correlation, Adaptation models, Image segmentation,
Feature extraction, Matrix decomposition, Gaussian distribution,
stepwise subtraction
BibRef
Guo, Q.L.[Qing-Le],
Zhang, J.P.[Jun-Ping],
Zhang, Y.[Ye],
Multitemporal Hyperspectral Images Change Detection Based on Joint
Unmixing and Information Coguidance Strategy,
GeoRS(59), No. 11, November 2021, pp. 9633-9645.
IEEE DOI
2111
Hyperspectral imaging, Perturbation methods, Feature extraction,
Training, Optimization, Task analysis, Data mining,
multitemporal information coguidance
BibRef
Li, Q.X.[Qiu-Xia],
Mu, T.K.[Ting-Kui],
Gong, H.[Hang],
Dai, H.S.[Hai-Shan],
Li, C.L.[Chun-Lai],
He, Z.P.[Zhi-Ping],
Wang, W.J.[Wen-Jing],
Han, F.[Feng],
Tuniyazi, A.[Abudusalamu],
Li, H.Y.[Hao-Yang],
Lang, X.[Xuechan],
Li, Z.Y.[Zhi-Yuan],
Wang, B.[Bin],
A Superpixel-by-Superpixel Clustering Framework for Hyperspectral
Change Detection,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Zhao, W.[Wei],
Wang, Z.R.[Zhi-Rui],
Gong, M.G.[Mao-Guo],
Liu, J.[Jia],
Discriminative Feature Learning for Unsupervised Change Detection in
Heterogeneous Images Based on a Coupled Neural Network,
GeoRS(55), No. 12, December 2017, pp. 7066-7080.
IEEE DOI
1712
Feature extraction, Image sensors, Neural networks,
Optical sensors, Remote sensing, Synthetic aperture radar,
heterogeneous images
BibRef
Zhao, H.Y.[Hong-Yu],
Feng, K.Y.[Kai-Yuan],
Wu, Y.[Yue],
Gong, M.G.[Mao-Guo],
An Efficient Feature Extraction Network for Unsupervised
Hyperspectral Change Detection,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Sun, J.[Jia],
Liu, J.[Jia],
Hu, L.[Ling],
Wei, Z.H.[Zhi-Hui],
Xiao, L.[Liang],
A Mutual Teaching Framework with Momentum Correction for Unsupervised
Hyperspectral Image Change Detection,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Sohail, M.[Muhammad],
Chen, Z.[Zhao],
Liu, G.H.[Guo-Hua],
Tensor ring with alternative change mask for multitemporal
hyperspectral image change detection,
PRL(164), 2022, pp. 46-52.
Elsevier DOI
2212
Multitemporal, Change detection, Tensor ring, Remote sensing
BibRef
Liu, S.[Song],
Li, H.W.[Hai-Wei],
Wang, F.F.[Fei-Fei],
Chen, J.Y.[Jun-Yu],
Zhang, G.[Geng],
Song, L.[Liyao],
Hu, B.L.[Bing-Liang],
Unsupervised Transformer Boundary Autoencoder Network for
Hyperspectral Image Change Detection,
RS(15), No. 7, 2023, pp. 1868.
DOI Link
2304
BibRef
Li, J.L.[Jin-Long],
Yuan, X.C.[Xiao-Chen],
Li, J.F.[Jin-Feng],
Huang, G.H.[Guo-Heng],
Feng, L.[Li],
Zhang, J.[Jing],
MV-CDN: Multi-Visual Collaborative Deep Network for Change Detection
of Double-Temporal Hyperspectral Images,
RS(15), No. 11, 2023, pp. 2834.
DOI Link
2306
BibRef
Hu, M.[Meiqi],
Wu, C.[Chen],
Du, B.[Bo],
Zhang, L.P.[Liang-Pei],
Binary Change Guided Hyperspectral Multiclass Change Detection,
IP(32), 2023, pp. 791-806.
IEEE DOI
2301
Correlation, Hyperspectral imaging, Feature extraction,
Optimization, Neural networks, Multitasking, Iterative methods,
deep neural network
BibRef
Li, X.R.[Xiao-Run],
Ding, J.G.[Ji-Gang],
Spectral-Temporal Transformer for Hyperspectral Image Change
Detection,
RS(15), No. 14, 2023, pp. 3561.
DOI Link
2307
BibRef
Hu, M.[Meiqi],
Wu, C.[Chen],
Zhang, L.P.[Liang-Pei],
GlobalMind: Global multi-head interactive self-attention network for
hyperspectral change detection,
PandRS(211), 2024, pp. 465-483.
Elsevier DOI
2405
Hyperspectral change detection, Transformer,
Global spatial correlation, Cross-temporal relevance, Self-attention
BibRef
Yang, B.[Bin],
Mao, Y.[Yin],
Liu, L.C.[Li-Cheng],
Fang, L.Y.[Le-Yuan],
Liu, X.X.[Xin-Xin],
Change Representation and Extraction in Stripes: Rethinking
Unsupervised Hyperspectral Image Change Detection With an Untrained
Network,
IP(33), 2024, pp. 5098-5113.
IEEE DOI
2410
Feature extraction, Data mining, Optimization, Data models,
Convolutional neural networks, Transforms, Hyperspectral imaging,
optimization
BibRef
Fang, L.Y.[Le-Yuan],
Li, S.T.[Shu-Tao],
Hu, J.W.[Jian-Wen],
Multitemporal image change detection with compressed sparse
representation,
ICIP11(2673-2676).
IEEE DOI
1201
BibRef
Oubara, A.[Amel],
Wu, F.[Falin],
Qu, G.X.[Guo-Xin],
Maleki, R.[Reza],
Yang, G.[Gongliu],
Enhancing Binary Change Detection in Hyperspectral Images Using an
Efficient Dimensionality Reduction Technique Within Adversarial
Learning,
RS(17), No. 1, 2025, pp. 5.
DOI Link
2501
BibRef
Resta, S.[Salvatore],
Acito, N.[Nicola],
Diani, M.[Marco],
Corsini, G.[Giovanni],
Opsahl, T.[Thomas],
Haavardsholm, T.V.[Trym Vegard],
Detection of small changes in airborne hyperspectral imagery:
Experimental results over urban areas,
MultiTemp11(5-8).
IEEE DOI
1109
BibRef
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Radar, SAR Image Change Detection .