From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques

• URL: http://arxiv.org/abs/2003.06583v1
• Date: Sat, 14 Mar 2020 09:24:08 GMT
• Title: From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques
• Authors: Bin Hou, Qingjie Liu, Heng Wang, and Yunhong Wang
• Abstract summary: We propose an end-to-end dual-branch architecture termed as the W-Net, with each branch taking as input one of the two bi-temporal images. We also apply the recently popular Generative Adversarial Network (GAN) in which our W-Net serves as the Generator. To train our networks and also facilitate future research, we construct a large scale dataset by collecting images from Google Earth.
• Score: 43.58400031452662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional change detection methods usually follow the image differencing, change feature extraction and classification framework, and their performance is limited by such simple image domain differencing and also the hand-crafted features. Recently, the success of deep convolutional neural networks (CNNs) has widely spread across the whole field of computer vision for their powerful representation abilities. In this paper, we therefore address the remote sensing image change detection problem with deep learning techniques. We firstly propose an end-to-end dual-branch architecture, termed as the W-Net, with each branch taking as input one of the two bi-temporal images as in the traditional change detection models. In this way, CNN features with more powerful representative abilities can be obtained to boost the final detection performance. Also, W-Net performs differencing in the feature domain rather than in the traditional image domain, which greatly alleviates loss of useful information for determining the changes. Furthermore, by reformulating change detection as an image translation problem, we apply the recently popular Generative Adversarial Network (GAN) in which our W-Net serves as the Generator, leading to a new GAN architecture for change detection which we call CDGAN. To train our networks and also facilitate future research, we construct a large scale dataset by collecting images from Google Earth and provide carefully manually annotated ground truths. Experiments show that our proposed methods can provide fine-grained change detection results superior to the existing state-of-the-art baselines.

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