Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images
Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples. In this article, a spatially self-paced convolutional network (SSPCN) is con...
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doaj-abe93d51148444b5a738e1bdbb5625a42021-06-03T23:08:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01144966497910.1109/JSTARS.2021.30784379427162Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous ImagesHao Li0https://orcid.org/0000-0002-6294-6761Maoguo Gong1https://orcid.org/0000-0002-0415-8556Mingyang Zhang2https://orcid.org/0000-0002-9768-516XYue Wu3https://orcid.org/0000-0002-3459-5079School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, ChinaSchool of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an, ChinaChange detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples. In this article, a spatially self-paced convolutional network (SSPCN) is constructed for change detection in an unsupervised way. Self-paced learning (SPL) is incorporated into convolutional networks to dynamically select reliable samples and learn the representation of the relations between the two heterogeneous images. In the proposed method, the pseudo labels are initialized by a classification-based method, and each sample is assigned to a weight to reflect the easiness of the sample. Then, SPL is used to learn the easy samples at first and then gradually take more complex samples into account. In the training process, the sample weights are dynamically updated based on the network parameters. Finally, a binary change map is acquired based on the trained convolutional network. The proposed SSPCN has three main advantages compared to the traditional methods. First, the proposed method is robust to noisy samples because the SSPCN involves the reliable samples into training. Second, the samples have different learning rates for converging to better values, and the learning rates are dynamically changed based on the current sample weights during iterations. Finally, we take the spatial information among the samples into account for further enhancing the robustness of the proposed method. Experimental results on four pairs of heterogeneous remote sensing images confirm the effectiveness of the proposed technique.https://ieeexplore.ieee.org/document/9427162/Change detectionconvolutional neural networks (CNNs)heterogeneous imagesself-paced learning (SPL) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hao Li Maoguo Gong Mingyang Zhang Yue Wu |
spellingShingle |
Hao Li Maoguo Gong Mingyang Zhang Yue Wu Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection convolutional neural networks (CNNs) heterogeneous images self-paced learning (SPL) |
author_facet |
Hao Li Maoguo Gong Mingyang Zhang Yue Wu |
author_sort |
Hao Li |
title |
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images |
title_short |
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images |
title_full |
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images |
title_fullStr |
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images |
title_full_unstemmed |
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images |
title_sort |
spatially self-paced convolutional networks for change detection in heterogeneous images |
publisher |
IEEE |
series |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
issn |
2151-1535 |
publishDate |
2021-01-01 |
description |
Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples. In this article, a spatially self-paced convolutional network (SSPCN) is constructed for change detection in an unsupervised way. Self-paced learning (SPL) is incorporated into convolutional networks to dynamically select reliable samples and learn the representation of the relations between the two heterogeneous images. In the proposed method, the pseudo labels are initialized by a classification-based method, and each sample is assigned to a weight to reflect the easiness of the sample. Then, SPL is used to learn the easy samples at first and then gradually take more complex samples into account. In the training process, the sample weights are dynamically updated based on the network parameters. Finally, a binary change map is acquired based on the trained convolutional network. The proposed SSPCN has three main advantages compared to the traditional methods. First, the proposed method is robust to noisy samples because the SSPCN involves the reliable samples into training. Second, the samples have different learning rates for converging to better values, and the learning rates are dynamically changed based on the current sample weights during iterations. Finally, we take the spatial information among the samples into account for further enhancing the robustness of the proposed method. Experimental results on four pairs of heterogeneous remote sensing images confirm the effectiveness of the proposed technique. |
topic |
Change detection convolutional neural networks (CNNs) heterogeneous images self-paced learning (SPL) |
url |
https://ieeexplore.ieee.org/document/9427162/ |
work_keys_str_mv |
AT haoli spatiallyselfpacedconvolutionalnetworksforchangedetectioninheterogeneousimages AT maoguogong spatiallyselfpacedconvolutionalnetworksforchangedetectioninheterogeneousimages AT mingyangzhang spatiallyselfpacedconvolutionalnetworksforchangedetectioninheterogeneousimages AT yuewu spatiallyselfpacedconvolutionalnetworksforchangedetectioninheterogeneousimages |
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1721398596182474752 |