Target detection based on classification in shadow region of hyperspectral image

Abstract

In the field of hyperspectral image (HSI) processing, shadow regions in HSIs are often ignored or simply treated as a category because of their low reflectivity and complex information. There have been some studies on shadow regions of HSIs but there are still few effective algorithms to detect the real substances under the shadow regions. In view of this problem and the good performance of convolution neural network (CNN) in HSI classification and target detection, this paper improved target detection method in shadow regions in HSI which combines the CNN and the adaptive coherence/cosine estimator (ACE) of the spectral derivative image. This method includes three main steps: firstly, shadow region would been determined by CNN model whose main parameters have been adjusted to optimize the network performance; secondly, the derivative data of hyperspectral image would be obtained by deriving the shadow region of hyperspectral image; finally, due to the prominent performance of ACE algorithm in target detection of HSI, this algorithm could be applied to detect the substances contained in the shadow regions. To assess the performance of the proposed method, one widely used HSI dataset is used in the experiments. The numbers of experiment results show that the proposed method can detect the substances under the shadow regions in HSIs and it also has promising prospect in the field of HSI data processing.