S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images

TL;DR

This paper introduces S2-cGAN, a self-supervised adversarial network that learns to detect changes in multispectral images without requiring extensive labeled data, by focusing on generating unchanged sample distributions.

Contribution

The paper presents a novel self-supervised cGAN approach that directly exploits discriminator likelihood for binary change detection in remote sensing images, reducing dependence on labeled datasets.

Findings

Outperforms existing change detection methods in experiments.

Effectively learns unchanged sample distribution without labeled data.

Demonstrates robustness across different multispectral datasets.

Abstract

Deep Neural Networks have recently demonstrated promising performance in binary change detection (CD) problems in remote sensing (RS), requiring a large amount of labeled multitemporal training samples. Since collecting such data is time-consuming and costly, most of the existing methods rely on pre-trained networks on publicly available computer vision (CV) datasets. However, because of the differences in image characteristics in CV and RS, this approach limits the performance of the existing CD methods. To address this problem, we propose a self-supervised conditional Generative Adversarial Network (S2-cGAN). The proposed S^2-cGAN is trained to generate only the distribution of unchanged samples. To this end, the proposed method consists of two main steps: 1) Generating a reconstructed version of the input image as an unchanged image 2) Learning the distribution of unchanged samples through an adversarial game. Unlike the existing GAN based methods (which only use the discriminator during the adversarial training to supervise the generator), the S2-cGAN directly exploits the discriminator likelihood to solve the binary CD task. Experimental results show the effectiveness of the proposed S2-cGAN when compared to the state of the art CD methods.