FCCDN: Feature Constraint Network for VHR Image Change Detection

• URL: http://arxiv.org/abs/2105.10860v1
• Date: Sun, 23 May 2021 06:13:47 GMT
• Title: FCCDN: Feature Constraint Network for VHR Image Change Detection
• Authors: Pan Chen, Danfeng Hong, Zhengchao Chen, Xuan Yang, Baipeng Li, Bing Zhang
• Abstract summary: We propose a feature constraint change detection network (FCCDN) for change detection. We constrain features both on bi-temporal feature extraction and feature fusion. We achieve state-of-the-art performance on two building change detection datasets.
• Score: 12.670734830806591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection is the process of identifying pixel-wise differences of bi-temporal co-registered images. It is of great significance to Earth observation. Recently, with the emerging of deep learning (DL), deep convolutional neural networks (CNNs) based methods have shown their power and feasibility 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 on bi-temporal 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 non-local feature pyramid network to extract and fuse multi-scale features. To fuse bi-temporal 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 two building change detection datasets (LEVIR-CD and WHU). On the LEVIR-CD dataset, we achieve IoU of 0.8569 and F1 score of 0.9229. On the WHU dataset, we achieve IoU of 0.8820 and F1 score of 0.9373. Moreover, we, for the first time, achieve the acquire of accurate bi-temporal semantic segmentation results without using semantic segmentation labels. It is vital for the application of change detection because it saves the cost of labeling.

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