SAR Image Change Detection in Spatial-Frequency Domain Based on Attention Mechanism and Gated Linear Unit

Abstract

Change detection based on synthetic aperture radar (SAR) images is an important application in the remote sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, firstly clustering is used as pre-classification to obtain pseudo-labels, and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. In order to improve the sensitivity of the algorithm to changed details and enhance the anti-noise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multi-domain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multi-region features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLU) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).