Hyperspectral change detection based on 3D convolution autoencoder and feature difference constraint

Hyperspectral image change detection is a hot research field. However, some methods only consider spectral information or spatial information. Besides, the feature extraction methods based on neural network usually need a large number of training samples, but it is difficult to collect labeled training samples. Therefore, a hyperspectral change detection method is proposed based on 3D convolution autoencoder and feature difference constraint. First, 3D convolution is used in the unsupervised autoencoder to extract deep spatial-spectral features. Then, in order to make spatial-spectral features not only retain the main information of the original data but also enhance the robustness to change discrimination, the loss function of the autoencoder is constructed based on reconstruction errors and feature difference constraints. Finally, using the extracted deep spatial-spectral features of bi-temporal hyperspectral images as input, a simple classification network is designed to judge whether the input feature pairs have changed. The inputted spatial-spectral features are low-dimensional, which can effectively reduce the parameters of the classification network. Therefore, only a small amount of label data is needed to complete the training, which improves the training efficiency and solves the problem of insufficient label data. Experiments conducted on three real hyperspectral datasets demonstrate that the proposed method achieves better detection performance than state of the arts.