Hyperspectral Target Detection in Noisy Environment Using Wavelet filter and Correlation based detector

In this paper, we propose an algorithm for detecting man made targets in hyperspectral imagery using correlation based detection after wavelet domain filtering. In the proposed method, each spectral pixel in noisy hyperspectral data cube is filtered by wavelet domain filtering. Wavelet domain filtering looks at every spectral pixel as noisy signal and filter out noise through wavelet shrinkage based method. Then correlation between the provided target spectral signature and spectral signal from data cube is calculated. The algorithm scans each pixel in data cube then calculates correlation with target signature. The process yields correlation image. Applying threshold operation for correlation image provides detection image. The detection performance of the algorithm is tested with several hyperspectral datasets. Using ROC analysis and comparing with ground truth image, it is observed that wavelet based filtering provides better detection performance for noisy data. The simulation results indicate that the proposed algorithm efficiently detects object of interest in all datasets.