A Semisupervised GAN-Based Multiple Change Detection Framework in Multi-Spectral Images

Effectively highlighting multiple changes in the earth surface from multi-temporal remote sensing images is a meaningful but challenging task. In order to reduce costs and ensure the performance, it is advisable to employ a semisupervised strategy to achieve this goal. As a discriminative joint classification task, semisupervised change detection aims to extract useful and discriminative features from a large amount of unlabeled data in addition to limited labeled samples. The discriminator of a well-trained generative adversarial network (GAN) is just right for this. Therefore, in this letter, we proposed a semisupervised GAN-based multiple change detection framework for multi-spectral images. First, the GAN is trained by all data without any prior information. Then, we combine two identical trained discriminators to construct a dual-pipeline joint classifier. Finally, the classifier is fine-tuned by a very small amount of labeled data to detect multiple changes. The superior performance of the proposed model over both real multi-spectral data sets demonstrates its robustness and effectiveness.