Unsupervised SAR Image Change Detection Based on Feature Fusion of Information Transfer
Jingjing Ma, Desheng Li, Xu Tang, Yuqun Yang, Xiangrong Zhang, Licheng Jiao
Abstract
Synthetic aperture radar (SAR) image change detection is a hot but challenging task due to SAR images’ complex contents and inherent speckle noises. The expected change detection methods should reduce the influence of speckle noises, obtain the discriminative feature representations, and generate accurate change maps simultaneously. To these ends, we propose a new SAR image change detection method named feature fusion of information transfer network (FFITN). First, we develop a hybrid convolution block to depress the speckle noise impacts and explore the valuable information from SAR images. Thus, the feature extraction module (FEM) is constructed to obtain the multi-level features. Then, an information transfer module (ITM) is proposed to capture the salient regions from various aspects. Also, the salient knowledge is transferred among features at different levels to enhance their discrimination. Next, a self-attention-based feature fusion module (SAFFM) is introduced to fuse various features. Finally, a change map generation module (CMGM) with the clustering algorithm and specific loss functions is designed to produce the pseudo labels and change maps. Experimental results on three public SAR data sets demonstrate the model’s effectiveness. Our source codes are available at https://github.com/TangXu-Group/FFITN.