  The fast development of self-supervised learning lowers the bar learning
feature representation from massive unlabeled data and has triggered a series
of research on change detection of remote sensing images. Challenges in
adapting self-supervised learning from natural images classification to remote
sensing images change detection arise from difference between the two tasks.
The learned patch-level feature representations are not satisfying for the
pixel-level precise change detection. In this paper, we proposed a novel
pixel-level self-supervised hyperspectral spatial-spectral understanding
network (HyperNet) to accomplish pixel-wise feature representation for
effective hyperspectral change detection. Concretely, not patches but the whole
images are fed into the network and the multi-temporal spatial-spectral
features are compared pixel by pixel. Instead of processing the two-dimensional
imaging space and spectral response dimension in hybrid style, a powerful
spatial-spectral attention module is put forward to explore the spatial
correlation and discriminative spectral features of multi-temporal
hyperspectral images (HSIs), separately. Only the positive samples at the same
location of bi-temporal HSIs are created and forced to be aligned, aiming at
learning the spectral difference-invariant features. Moreover, a new similarity
loss function named focal cosine is proposed to solve the problem of imbalanced
easy and hard positive samples comparison, where the weights of those hard
samples are enlarged and highlighted to promote the network training. Six
hyperspectral datasets have been adopted to test the validity and
generalization of proposed HyperNet. The extensive experiments demonstrate the
superiority of HyperNet over the state-of-the-art algorithms on downstream
hyperspectral change detection tasks.
