Spectral-Spatial Enhanced Local Contrast Strategy for Hyperspectral Small Air Target Detection

Detecting small air target is an important task in civil aviation. However, the weak characteristics of these targets make detection challenging. Hyperspectral image (HSI), provides a new approach for the small air target detection task due to its strong ability of capturing both spatial and spectral information simultaneously. In this article, we propose a spectral-spatial enhanced local contrast strategy for hyperspectral small air target detection. An unsupervised band selection step based on the local contrast strategy has been designed based on local contrast (LC-UBSM) to choose bands with better distinguish ability between the target and background in HSI. Then, we have developed an improved RX detection algorithm with combined spatial and spectral variance (CSSV-RX) to detect the target while suppressing both background and noise. Experimental results on both real GAOFEN-5 dataset and simulated dataset based on EO-1 (Earth Observing-1) satellite have validated the effectiveness and robustness of the proposed method.