Hyperspectral Detection and Unmixing of Subpixel Target Using Iterative Constrained Sparse Representation

This is the authors' abstract. We don't add key points for this paper.

With great significance in military and civilian applications, subpixel target detection is of great interest in hyperspectral remote sensing. The subpixel targets usually also need to be unmixed to identify their components. Traditionally, these subpixel targets are first detected and then unmixed to obtain their corresponding abundances. Therefore, target detection and target unmixing are independently performed. However, there are potential relations between these two processes that need to be investigated. In this article, we integrate these two processes using iterative constrained sparse representation. The main idea of this algorithm is that each pixel can be linearly and sparsely represented by the prior target spectra and several background endmembers extracted from its neighborhood. Moreover, the sum-to-one and nonnegativity constraints are introduced to ensure the sparse representation coefficients to have physical meaning. Specifically, the background endmembers are automatically extracted from the local background based on an iterative process. Then, the test pixel is represented by these extracted endmembers. Finally, the detection output is determined by the total target abundance and the residuals. The main innovation of this method is that it implements detection and unmixing of subpixel target simultaneously, even if the local background is contaminated by target signals. Experiments conducted on both synthetic and real hyperspectral datasets demonstrate that the proposed detector achieves an outstanding performance on detection and unmixing.