A target detection method for hyperspectral image based on mixture noise model

Subpixel hyperspectral detection is a kind of method which tries to locate targets in a hyperspectral image when the spectrum of the targets is given. Due to its subpixel nature, targets are often smaller than one pixel, which increases the difficulty of detection. Many algorithms have been proposed to tackle this problem, most of which model the noise in all spatial points of hyperspectral image by multivariate normal distribution. However, this model alone may not be an appropriate description of the noise distribution in hyperspectral image. After carefully studying the distribution of hyperspectral image, it is concluded that the gradient of noise also obeys normal distribution. In this paper two detectors are proposed: mixture gradient structured detector (MGSD) and mixture gradient unstructured detector (MGUD). These detectors are based on a new model which takes advantage of the distribution of the gradient of the noise. This makes the detectors more accordant with the practical situation. To evaluate the performance of the proposed detectors, three different data sets, including one synthesized data set and two real-world data sets, are used in the experiments. Results show that the proposed detectors have better performance than current subpixel detectors.