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In Optics express

Most of the existing deep learning methods for hyperspectral image (HSI) classification use pixel-wise or patch-wise classification. In this paper, we propose an image-wise classification method, where the network input is the original hyperspectral cube rather than the spectral curve of each pixel (i.e., pixel-wise) or neighbor region of each pixel (i.e., patch-wise). Specifically, we propose a minimalistic fully convolution network (MFCN) and a semi-supervised loss function, which can perform pixel-level classification for HSI with few labeled samples. The comparison experiments demonstrated the progress of our methods, using three new benchmark HSI datasets (WHU-Hi-LongKou, WHU-Hi-HanChuan and WHU-Hi-HongHu) with wavelength range from 400 to 1000nm. In the comparison experiments, we randomly selected 25 labeled pixels from each class for training, equivalent to only 0.11%, 0.16%, and 0.14% of all labeled pixels for the three datasets, respectively. In addition, through ablation studies and theoretical analysis, we verified and analyzed the effectiveness and superiority of our design choices.

Xu Buyun, Hou Weijun, Wei Yiwei, Wang Yiting, Li Xihai