Hyperspectral Target Detection with CNN Using Subtraction Model

Recently, the convolutional neural network (CNN) has been widely used in the fields of hyperspectral image (HSI) processing. In this paper, a CNN-based hyperspectral target detection framework is presented. And subtraction model is used to sufficiently enlarge the number of training samples. The subtraction model is built from twenty-eight manually selected objects in several AVIRIS date following three aspects: 1) The new pixel made by subtraction of any two pixels between 27 different classes is labelled as 0; 2) the new pixel made by subtraction of any two pixels within per class is labelled as 0; 3) the new pixel made by subtraction of any two pixels, in which one pixel is from the target class and the other is from background classes, is labelled as 1. Theoretically, if the pixel under test belongs to the target class, the output label of the CNN will be the same as the label of the target class. The experiment results on three images all indicate that the proposed CNN-based detector outperforms the classical hyperspectral target detection algorithms.