Spectral-Spatial Joint Target Detection of Hyperspectral Image Based on Transfer Learning

Hyperspectral image (HSI) target detection has become an increasingly important research topic while still facing various aspects of difficulties, such as the influence of spectral variability, the deficiency of available samples and the limited capability of utilizing spectral-spatial information. In this paper, a novel HSI target detection method combining spectral and spatial information is proposed. Siamese convolutional neural network (S-CNN) is applied as spectral feature extractor according to the spectral similarity, in which pixel pairs generated from the source domain HSI are transferred to target domain so as to solve the problem of insufficient samples. Then spatial post-processing guided by the initial result is used to combining spatial context information to further improve the detection performance. Experimental results show that the proposed method performs well in hyperspectral target detection.