Advance and Future Challenges in Hyperspectral Target Detection

In this review，recent developments and future challenges in hyperspectral target detection are considered in relation to the two main approaches.The signal detection framework induced methods such as the structured backgrounds detector constrained energy minimization(CEM) and the unstructured backgrounds detector adaptive cosine/coherent estimation(ACE) are the classical methods of hyperspectral target detection，while advanced statistical pattern recognition and machine learning based approaches such as the kernel method and the sparse representation related algorithms are becoming the frontier topic in this area. The core concepts of these methods as well as their advantages and disadvantages are overviewed，and the future prospects of hyperspectral target detection are out lined.