FCCDN: Feature constraint network for VHR image change detection
Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective superv...
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| Published in | ISPRS journal of photogrammetry and remote sensing Vol. 187; pp. 101 - 119 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0924-2716 1872-8235 |
| DOI | 10.1016/j.isprsjprs.2022.02.021 |
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| Abstract | Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. |
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| AbstractList | Change detection is of great significance to Earth observations. Recently, with the emergence of deep learning (DL), the power and feasibility of deep convolutional neural network (CNN)-based methods have been shown in the field of change detection. However, there is still a lack of effective supervision for change feature learning. In this work, a feature constraint change detection network (FCCDN) is proposed. We constrain features both in bitemporal feature extraction and feature fusion. More specifically, we propose a dual encoder-decoder network backbone for the change detection task. At the center of the backbone, we design a nonlocal feature pyramid network to extract and fuse multiscale features. To fuse bitemporal features in a robust way, we build a dense connection-based feature fusion module. Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning. Based on FCCDN, we achieve state-of-the-art performance on three change detection datasets (LEVIR-CD, WHU, and SECOND). The experimental results show that FCCDN outperforms all benchmark methods. Moreover, for the first time, the acquisition of accurate bitemporal semantic segmentation results is achieved without using semantic segmentation labels. This is vital for the application of change detection because it saves the cost of labeling. The code of this work can be found on https://github.com/chenpan0615/FCCDN_pytorch. |
| Author | Chen, Pan Hong, Danfeng Yang, Xuan Zhang, Bing Li, Baipeng Chen, Zhengchao |
| Author_xml | – sequence: 1 givenname: Pan surname: Chen fullname: Chen, Pan organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 2 givenname: Bing surname: Zhang fullname: Zhang, Bing email: zb@radi.ac.cn organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 3 givenname: Danfeng surname: Hong fullname: Hong, Danfeng organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 4 givenname: Zhengchao surname: Chen fullname: Chen, Zhengchao organization: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 5 givenname: Xuan surname: Yang fullname: Yang, Xuan organization: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China – sequence: 6 givenname: Baipeng surname: Li fullname: Li, Baipeng organization: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China |
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