BACKGROUND GUIDED TARGET DETECTION FOR HYPERSPECTRAL IMAGE

Target detection has become an increasingly important research topic in hyperspectral image (HSI) processing with most existing detecting algorithms focus on the information of required targets while fail to utilize the background of input data. In this paper, we propose a novel target detection method for HSI, which deals with the detection problem from a new contextual perspective. Based on an initial detection result, the background of HSI is extracted by introducing the joint convolutional analysis and synthesis (JCAS) sparse representation method. Then, the initial result is corrected through a background guided approach, false alarm is greatly reduced. Experimental results show that the proposed method performs well in hyperspectral target detection.