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Detecting multiple changes from remote sensing imagery is a research hotspot. Very high resolution (VHR) images contain detailed spatial information and thus are often used in multiple change detection (CD). Compared with supervised multiple CD methods, unsupervised methods are more attractive, due to the ability of extracting changes automatically. However, many existing unsupervised methods fail to well adaptively make use of the high-level features relevant to multiple changes in VHR images in some cases. In this paper, a novel unsupervised multiple CD method for VHR images is proposed. First, the magnitude of spectral change vectors (SCVs) is calculated by change vector analysis, and fuzzy c-means clustering is performed to generate the unchanged and candidate changed samples. Secondly, the candidate changed samples are further clustered based on the direction of SCVs, and multiple changed samples are selected using a local window. Finally, image patches composed of neighbourhood areas of the generated samples are fed into a convolutional neural network (CNN) for training, and the multiple change map is obtained by the trained CNN. Experiments were performed on four data sets, and results indicated that the proposed unsupervised multiple CD approach outperformed some other state-of-the-art methods.

A novel unsupervised multiple change detection method for VHR remote sensing imagery using CNN with hierarchical sampling