12.1.5.2 Change Detection for Hyperspectral Images

Chapter Contents (Back)
Change Detection. Hyperspectral Images.

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.[Jigang],
Spectral-Temporal Transformer for Hyperspectral Image Change Detection,
RS(15), No. 14, 2023, pp. 3561.
DOI Link 2307
BibRef


Seydi, S.T., Hasanlou, M.,
Binary Hyperspectral Change Detection Based on 3d Convolution Deep Learning,
ISPRS20(B3:1629-1633).
DOI Link 2012
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 .


Last update:Apr 18, 2024 at 11:38:49