Local Descriptor Learning for Change Detection in Synthetic Aperture Radar Images via Convolutional Neural Networks

In this paper, we present a novel convolutional neural network (CNN)-based model for change detection in synthetic aperture radar (SAR) images. Considering that change detection task takes image pairs as an input, we first explore multiple neural network architectures, which are specifically adapted to the change detection task. There are several ways in which patch pairs can be processed by the network and how information sharing can efficiently learn the semantic difference between the changed and unchanged pixels. For this reason, we then design a "Siamese samples" CNN, which treats patch pairs as indiscriminate samples to extract descriptors and then joins for their outputs. During training, the two patch features are extracted by the same network instead of separate sub-networks, while the joining neuron measures the distance between the two feature vectors. Due to "pseudo-labels" with high accuracy that is difficult to obtain, we modify a joint classifier based on the fuzzy c-means method into joint-similarity classifier as preclassification to obtain coarse "pseudo labels," and discard sample selection. Thus, the preclassification labels with a low accuracy are used to fine-tune the network. Finally, a significantly improved change detection result can be obtained from the network. The proposed architecture provides a better trade-off in terms of speed and accuracy among its counterparts (Siamese, Pseudo-Siamese, and 2-Channel networks). The experiments on several real SAR data sets demonstrate the state-of-the-art performance of the proposed method compared with the advanced change detection methods.