Unsupervised sparse convolutional autoencoder for multi-class change detection in hyperspectral images

Change Detection (CD) approaches for hyperspectral images (HSI) are mainly unsupervised and hierarchically extract the endmembers to determine the multiple change classes but require many parameters to set manually. Recently, HSI CD has been approached with DL methods because of their capacity to learn features of changes automatically, but they require a huge amount of labeled data for weakly or fully supervised training. They mostly perform binary CD only and do not fully exploit the spectral information. Accordingly, we propose an unsupervised DL CD method to identify multiple change classes in bi-temporal HSIs, inspired by a sparse autoencoder for spectral unmixing. The proposed method learns the endmembers of the unchanged class and the various classes of change by solving an unmixing problem with a Convolutional Autoencoder (CAE) trained in an unsupervised way using unlabeled patches sampled from the difference of the bi-temporal HSIs. The spectral unmixing problem is solved by applying three constraints to the CAE: a sparsity l21-norm constraint that forces the model to learn non-redundant information, a non-negativity constraint, and the sum-to-one constraint. After the training, we process the difference image with the trained Autoencoder to extract the abundance maps of the various change types being derived from the endmembers learned by the model during the training. A Change Vector Analysis approach detects the changed areas that are clustered with an X-means approach using the change abundances to obtain a multi-class change map. We obtained promising results by testing the proposed method on bi-temporal Hyperion images acquired on Benton County, Washington, USA, in May 2004 and May 2007, and bi-temporal PRISMA images acquired on an area close to Vienna in April 2020 and September 2021 that show the changes in crop fields.