Change Detection from SAR Images Based on Deformable Residual Convolutional Neural Networks

TL;DR

This paper introduces DRNet, a deformable residual CNN that adaptively adjusts sampling locations and enhances multi-scale feature extraction for improved SAR image change detection.

Contribution

The paper proposes a novel deformable residual CNN with adaptive sampling and a hierarchical pooling module for better change detection in SAR images.

Findings

DRNet outperforms existing methods on three SAR datasets.

Deformable convolution improves spatial structure modeling.

Hierarchical pooling enhances multi-scale feature representation.

Abstract

Convolutional neural networks (CNN) have made great progress for synthetic aperture radar (SAR) images change detection. However, sampling locations of traditional convolutional kernels are fixed and cannot be changed according to the actual structure of the SAR images. Besides, objects may appear with different sizes in natural scenes, which requires the network to have stronger multi-scale representation ability. In this paper, a novel \underline{D}eformable \underline{R}esidual Convolutional Neural \underline{N}etwork (DRNet) is designed for SAR images change detection. First, the proposed DRNet introduces the deformable convolutional sampling locations, and the shape of convolutional kernel can be adaptively adjusted according to the actual structure of ground objects. To create the deformable sampling locations, 2-D offsets are calculated for each pixel according to the spatial information of the input images. Then the sampling location of pixels can adaptively reflect the spatial structure of the input images. Moreover, we proposed a novel pooling module replacing the vanilla pooling to utilize multi-scale information effectively, by constructing hierarchical residual-like connections within one pooling layer, which improve the multi-scale representation ability at a granular level. Experimental results on three real SAR datasets demonstrate the effectiveness of the proposed DRNet.