22.2.8.1 Land Cover Change Analysis Using Learning, Neural Nets

Chapter Contents (Back)
Classification. Change Detection. Land Cover. Temporal Analysis. Remote Sensing. Learning. Neural Networks.
See also Land Cover Change Analysis, Remote Sensing Change Analysis, Temporal Analysis.
See also Change Detection for Remote Sensing Image Level.

Bruzzone, L., Cossu, R.,
A multiple-cascade-classifier system for a robust and partially unsupervised updating of land-cover maps,
GeoRS(40), No. 9, September 2002, pp. 1984-1996.
IEEE Top Reference. 0212
BibRef

Bruzzone, L., Cossu, R., Vernazza, G.,
Detection of land-cover transitions by combining multidate classifiers,
PRL(25), No. 13, 1 October 2004, pp. 1491-1500.
Elsevier DOI 0410
Find changes. BibRef

Bruzzone, L.[Lorenzo], Marconcini, M.[Mattia],
Toward the Automatic Updating of Land-Cover Maps by a Domain-Adaptation SVM Classifier and a Circular Validation Strategy,
GeoRS(47), No. 4, April 2009, pp. 1108-1122.
IEEE DOI 0903
BibRef

Bruzzone, L.[Lorenzo], Marconcini, M.[Mattia],
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy,
PAMI(32), No. 5, May 2010, pp. 770-787.
IEEE DOI 1003
Training data available only for a related domain. Domain Adaption SVM. BibRef

Persello, C., Bruzzone, L.,
A Novel Protocol for Accuracy Assessment in Classification of Very High Resolution Images,
GeoRS(48), No. 3, March 2010, pp. 1232-1244.
IEEE DOI 1003
BibRef

Demir, B., Persello, C., Bruzzone, L.,
Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images,
GeoRS(49), No. 3, March 2011, pp. 1014-1031.
IEEE DOI 1103
BibRef

Demir, B., Bruzzone, L.,
A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval,
GeoRS(53), No. 5, May 2015, pp. 2323-2334.
IEEE DOI 1502
feedback BibRef

Demir, B., Bruzzone, L.,
Hashing-Based Scalable Remote Sensing Image Search and Retrieval in Large Archives,
GeoRS(54), No. 2, February 2016, pp. 892-904.
IEEE DOI 1601
Hamming distance BibRef

Persello, C., Bruzzone, L.,
Active Learning for Domain Adaptation in the Supervised Classification of Remote Sensing Images,
GeoRS(50), No. 11, November 2012, pp. 4468-4483.
IEEE DOI 1210
BibRef

Niazmardi, S., Demir, B., Bruzzone, L., Safari, A., Homayouni, S.,
Multiple Kernel Learning for Remote Sensing Image Classification,
GeoRS(56), No. 3, March 2018, pp. 1425-1443.
IEEE DOI 1804
image classification, image fusion, learning (artificial intelligence), remote sensing, remote sensing (RS) BibRef

Persello, C., Bruzzone, L.,
Active and Semisupervised Learning for the Classification of Remote Sensing Images,
GeoRS(52), No. 11, November 2014, pp. 6937-6956.
IEEE DOI 1407
Context BibRef

Persello, C., Bruzzone, L.,
Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning,
GeoRS(54), No. 5, May 2016, pp. 2615-2626.
IEEE DOI 1604
geophysical techniques
See also Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples, A. BibRef

Demir, B.[Begum], Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Detection of Land-Cover Transitions in Multitemporal Remote Sensing Images With Active-Learning-Based Compound Classification,
GeoRS(50), No. 5, May 2012, pp. 1930-1941.
IEEE DOI 1202
BibRef

Bruzzone, L., Bovolo, F.,
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images,
PIEEE(100), No. 3, March 2013, pp. 609-630.
IEEE DOI 1303
BibRef

Demir, B.[Begum], Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach,
GeoRS(51), No. 1, January 2013, pp. 300-312.
IEEE DOI 1301
BibRef
Earlier:
Active-learning based cascade classification of multitemporal images for updating land-cover maps,
MultiTemp11(57-60).
IEEE DOI 1109

See also Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment, A. BibRef

Demir, B.[Begum], Bovolo, F.[Francesca], Bruzzone, L.[Lorenzo],
Classification of Time Series of Multispectral Images with Limited Training Data,
IP(22), No. 8, 2013, pp. 3219-3233.
IEEE DOI statistical distributions; time series; limited training data; supervised classifier; active learning; automatic classification; cascade classification; land-cover maps; transfer learning 1307
BibRef

Sudheer, K., Gowda, P., Chaubey, I., Howell, T.,
Artificial Neural Network Approach for Mapping Contrasting Tillage Practices,
RS(2), No. 2, February 2010, pp. 579-590.
DOI Link 1203
BibRef

Lyu, H.[Haobo], Lu, H.[Hui], Mou, L.C.[Li-Chao],
Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection,
RS(8), No. 6, 2016, pp. 506.
DOI Link 1608
BibRef

Ji, S.P.[Shun-Ping], Zhang, C.[Chi], Xu, A.J.[An-Jian], Shi, Y.[Yun], Duan, Y.L.[Yu-Lin],
3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Zhu, R.J.[Ruo-Jin], Yu, D.[Dawen], Ji, S.P.[Shun-Ping], Lu, M.[Meng],
Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Kong, Y.L.[Yun-Long], Huang, Q.Q.[Qing-Qing], Wang, C.Y.[Cheng-Yi], Chen, J.B.[Jing-Bo], Chen, J.S.[Jian-Sheng], He, D.X.[Dong-Xu],
Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Rußwurm, M.[Marc], Körner, M.[Marco],
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders,
IJGI(7), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef
Earlier: Multi-temporal Land Cover Classification With Long Short-term Memory Neural Networks Hannover17(551-558).
DOI Link 1805
BibRef

Pelletier, C.[Charlotte], Webb, G.I.[Geoffrey I.], Petitjean, F.[François],
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Lee, J.[Junghee], Han, D.[Daehyeon], Shin, M.[Minso], Im, J.[Jungho], Lee, J.[Junghye], Quackenbush, L.J.[Lindi J.],
Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Wang, B.[Biao], Choi, J.[Jaewan], Choi, S.[Seokeun], Lee, S.[Soungki], Wu, P.[Penghai], Gao, Y.[Yan],
Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Song, A.[Ahram], Choi, J.[Jaewan],
Fully Convolutional Networks with Multiscale 3D Filters and Transfer Learning for Change Detection in High Spatial Resolution Satellite Images,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Kim, Y.[Yeseul], Park, N.W.[No-Wook], Lee, K.D.[Kyung-Do],
Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Salmon, B.P., Holloway, D.S., Kleynhans, W., Olivier, J.C., Wessels, K.J.,
Applying Model Parameters as a Driving Force to a Deterministic Nonlinear System to Detect Land Cover Change,
GeoRS(55), No. 12, December 2017, pp. 7165-7176.
IEEE DOI 1712
Feature extraction, Force, Learning systems, MODIS, Remote sensing, Satellites, Time series analysis, Nonlinear detection, time series BibRef

Interdonato, R.[Roberto], Ienco, D.[Dino], Gaetano, R.[Raffaele], Ose, K.[Kenji],
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn,
PandRS(149), 2019, pp. 91-104.
Elsevier DOI 1903
Satellite image time series, Deep learning, Land cover classification, Sentinel-2 BibRef

Zhang, P., Gong, M., Zhang, H., Liu, J., Ban, Y.,
Unsupervised Difference Representation Learning for Detecting Multiple Types of Changes in Multitemporal Remote Sensing Images,
GeoRS(57), No. 4, April 2019, pp. 2277-2289.
IEEE DOI 1904
Gaussian distribution, geophysical image processing, image classification, image resolution, neural nets, remote sensing BibRef

Huang, L.J.[Li-Jun], An, R.[Ru], Zhao, S.Y.[Sheng-Yin], Jiang, T.[Tong], Hu, H.[Hao],
A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Dong, H.[Huihui], Ma, W.P.[Wen-Ping], Wu, Y.[Yue], Zhang, J.[Jun], Jiao, L.C.[Li-Cheng],
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Rußwurm, M.[Marc], Körner, M.[Marco],
Self-attention for raw optical Satellite Time Series Classification,
PandRS(169), 2020, pp. 421-435.
Elsevier DOI 2011
Self-attention, Transformer, Time series classification, Multitemporal Earth observation, Crop type mapping, Deep learning BibRef

Seydi, S.T.[Seyd Teymoor], Hasanlou, M.[Mahdi], Amani, M.[Meisam],
A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Yang, S.T.[Shu-Ting], Gu, L.J.[Ling-Jia], Li, X.F.[Xiao-Feng], Jiang, T.[Tao], Ren, R.Z.[Rui-Zhi],
Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Sefrin, O.[Oliver], Riese, F.M.[Felix M.], Keller, S.[Sina],
Deep Learning for Land Cover Change Detection,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Msellmi, B.[Bouthayna], Picone, D.[Daniele], Ben Rabah, Z.[Zouhaier], Mura, M.D.[Mauro Dalla], Farah, I.R.[Imed Riadh],
Sub-Pixel Mapping Model Based on Total Variation Regularization and Learned Spatial Dictionary,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Yasrab, R.[Robail], Zhang, J.C.[Jin-Cheng], Smyth, P.[Polina], Pound, M.P.[Michael P.],
Predicting Plant Growth from Time-Series Data Using Deep Learning,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Hu, T.X.[Tong-Xi], Myers Toman, E.[Elizabeth], Chen, G.[Gang], Shao, G.[Gang], Zhou, Y.[Yuyu], Li, Y.[Yang], Zhao, K.G.[Kai-Guang], Feng, Y.[Yinan],
Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine,
PandRS(176), 2021, pp. 250-261.
Elsevier DOI 2106
Google Earth Engine, Working landscape, Ensemble learning, Change detection, Hydraulic fracturing, BEAST, Sub-pixel BibRef


Déprez, A., Puissant, A., Malet, J.P., Michéa, D.,
Imclass: A User-tailored Machine Learning Image Classification Chain For Change Detection Or Landcover Mapping,
ISPRS20(B3:677-683).
DOI Link 2012
BibRef

Sainte Fare Garnot, V., Landrieu, L., Giordano, S., Chehata, N.,
Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention,
CVPR20(12322-12331)
IEEE DOI 2008
Satellites, Agriculture, Time series analysis, Feature extraction, Computer architecture, Machine learning BibRef

Jing, S., Chao, T.,
Time Series Land Cover Classification Based on Semi-supervised Convolutional Long Short-term Memory Neural Networks,
ISPRS20(B2:1521-1528).
DOI Link 2012
BibRef

Kamdem de Teyou, G., Tarabalka, Y., Manighetti, I., Almar, R., Tripodi, S.,
Deep Neural Networks for Automatic Extraction of Features In Time Series Optical Satellite Images,
ISPRS20(B2:1529-1535).
DOI Link 2012
BibRef

Babaeian Diva, A., Bigdeli, B., Pahlavani, P.,
Agricultural Land Change Detecting and Forecasting Using Combination Of Feedforward Multilayer Neural Network, Cellular Automata and Markov Chain Models,
SMPR19(153-158).
DOI Link 1912
BibRef

Grivei, A.C., Radoi, A., Datcu, M.,
Land cover change detection in Satellite Image Time Series using an active learning method,
MultiTemp17(1-4)
IEEE DOI 1712
geophysical image processing, land cover, support vector machines, terrain mapping, time series, Support vector machines BibRef

El Amin, A.M., Liu, Q., Wang, Y.,
Zoom out CNNs features for optical remote sensing change detection,
ICIVC17(812-817)
IEEE DOI 1708
Fish, Image segmentation, Optical imaging, Optical sensors, change detection, convolutional neural network, deep learning, remote sensing BibRef

Burchfield, E.[Emily], Nay, J.J.[John J.], Gilligan, J.[Jonathan],
Application Of Machine Learning To The Prediction Of Vegetation Health,
ISPRS16(B2: 465-469).
DOI Link 1610
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Changes using Landsat Images .


Last update:Nov 30, 2021 at 22:19:38