A Classified Adversarial Network for Multi-Spectral Remote Sensing Image Change Detection

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-07, Vol.12 (13), p.2098
• Main Authors: Wu, Yue, Bai, Zhuangfei, Miao, Qiguang, Ma, Wenping, Yang, Yuelei, Gong, Maoguo
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Change detection; Classification; Clustering; Deep learning; generative adversarial networks (GANs); Image detection; Image resolution; Machine learning; Methods; multi-spectral remote sensing image; Neural networks; Principal components analysis; Remote sensing; Spectra; Training
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12132098

Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the distribution of the bitemporal multi-spectral image data and transforms it into change detection results, and these change detection results (as the fake data) are input into the discriminator to train the discriminator. The results obtained by pre-classification are also input into the discriminator as the real data. The adversarial training can facilitate the generator learning the transformation from a bitemporal image to a change map. When the generator is trained well, the generator has the ability to generate the final result. The bitemporal multi-spectral images are input into the generator, and then the final change detection results are obtained from the generator. The proposed method is completely unsupervised, and we only need to input the preprocessed data that were obtained from the pre-classification and training sample selection. Through adversarial training, the generator can better learn the relationship between the bitemporal multi-spectral image data and the corresponding labels. Finally, the well-trained generator can be applied to process the raw bitemporal multi-spectral images to obtain the final change map (CM). The effectiveness and robustness of the proposed method were verified by the experimental results on the real high-resolution multi-spectral image data sets.