Hyperspectral Target Detection With RoI Feature Transformation

Hyperspectral target detection has been widely used in practice, but its performance is seriously affected by spatial redundancy and spectral variation. In this paper, we design a novel network block including the region of interest (RoI) feature transformation (RFT) and convolution layer, which is called RS block, that can automatically attach different importance to pixels and provide guidance to discriminative feature extraction. Furthermore, a deep neural network (termed as RFTD) is proposed by stacking several RS blocks for hyperspectral target detection. In this way, the RS block enforces RFTD to concentrate on RoI feature extraction, and further increase distinction between target and hard-detected background (false alarm) pixels. Additionally, a constraint loss is introduced to exploit the sparsity and low rank property of hyperspectral images (HSI). Finally, we apply nearest neighbors (NN) for target detection in the feature space. Experimental results on two HSIs demonstrate that the proposed RFTD algorithm outperforms other detection methods.