LAUSR

Currently, most of the supervised change detection approaches require a training data set that contains samples from both the changed and the unchanged data. However, under certain condition, such as natural disaster and military attack, the changed data samples are very few or even not available but the unchanged data are abundant. In this letter, we develop a generative adversarial networks (GANs)-based one-class classification (OCC) technique for time series remote-sensing image change detection. The proposed method is only trained with the unchanged data instead of both the changed and unchanged data. To achieve this purpose, first, spatial-spectral features are extracted from the time series remote-sensing images. Second, a GAN model is trained to detect the changes only with the extracted features of unchanged data. Remarkably, to offset the outlier errors caused by the incomplete supervision information provided by unchanged data alone, changed data, instead of unchanged data, are generated to improve power of discriminator. Finally, testing data are classified by the trained discriminator of GAN to produce a binary change map. Experimental results obtained on two optical time series remote-sensing data sets confirmed the effectiveness of our proposed method.