Abstract
Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness.
Highlights
Published: 7 September 2021Change detection (CD) in remote sensing images is essentially the detection of change information of the ground’s surface at different time phases [1]
It contributes to the robustness of the model against rotation distortion, with a better generalization ability, without introducing additional hyperparameters and inference times; We propose a remote sensing image CD network, which is based on the combined attention and asymmetric convolution block (ACB)
T2 images; images; (c) are results obtained by are results obtained by FC-EF; (f) are results obtained by FC-Siam-diff; (g) are are label; (d) are results obtained by CDNet; (e) are results obtained by FC-EF; (f) are results obtained by FC-Siam-diff; (g) results obtained by FC-Siam-conc; (h) are results obtained by fully convolutional networks (FCN)-PP; (i) are results obtained by CD-UNet++; (j) are results results obtained by FC-Siam-conc; (h) are results obtained by FCN-PP; (i) are results obtained by CD-UNet++; (j) are results obtained by STANet; (k) are results obtained by CANet
Summary
Change detection (CD) in remote sensing images is essentially the detection of change information of the ground’s surface at different time phases [1]. CD-UNet++ [21] proposed an end-to-end CNN structure for the CD of high-resolution satellite images and proposed a new UNet++ model, which can achieve deep supervision and capture subtle changes in complex changed scenes In their method, deep supervision enhances the characterization and identification performance of shallow features. The main contributions of this article are as follows: We propose a combined attention mechanism, which leverages spatial, channel, and position information to discriminate fine features, so as to obtain rich information of detected objects The application of this mechanism improves the detection performance of small targets: 2. We introduce an asymmetric convolution block (ACB) in our model It contributes to the robustness of the model against rotation distortion, with a better generalization ability, without introducing additional hyperparameters and inference times; We propose a remote sensing image CD network, which is based on the combined attention and ACB.
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