  Hyperspectral target detection is a pixel-level recognition problem. Given a
few target samples, it aims to identify the specific target pixels such as
airplane, vehicle, ship, from the entire hyperspectral image. In general, the
background pixels take the majority of the image and complexly distributed. As
a result, the datasets are weak annotated and extremely imbalanced. To address
these problems, a spectral mixing based self-supervised paradigm is designed
for hyperspectral data to obtain an effective feature representation. The model
adopts a spectral similarity based matching network framework. In order to
learn more discriminative features, a pair-based loss is adopted to minimize
the distance between target pixels while maximizing the distances between
target and background. Furthermore, through a background separated step, the
complex unlabeled spectra are downsampled into different sub-categories. The
experimental results on three real hyperspectral datasets demonstrate that the
proposed framework achieves better results compared with the existing
detectors.
