Depthwise Separable Convolutional Autoencoders for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI-CD) has recently become a research hotspot. Current methods rely heavily on a huge amount of training samples to perform the change detection tasks. While acquiring data from the same region of bitemporal HSIs is extraordinarily time-consuming and laborious. Therefore, this letter proposes an unsupervised method based on 3-D depthwise separable convolutional autoencoders (DSConvAEs). First, the dual-branch symmetrical 3-D (DBS3-D) DSConvAE is pretrained with limited samples to obtain the optimal weights, which facilitates extracting discriminative spatial and spectral features subsequently. Second, we adopt the temporal-specific feature concatenation strategy to acquire comprehensive characteristics from bi-temporal HSIs. Third, the general autoencoders are employed at the end of the model to further explore the high-level and abstract feature vectors. Finally, we compare the mean square loss calculated from the spatial-spectral branches and apply threshold judgment to generate the ultimate detection maps. Experimental results on three public HSI datasets demonstrate that the proposed framework outperforms other comparative methods by significant improvements.