Sparsity Based for Target Detection of Hyperspectral Image

Band selection (BS) plays an important role in the dimensionality reduction of hyperspectral data. However, as to the existing BS methods, few are specially designed for target detection. In this letter, we combine the target detection and BS process together and put forward a new BS method for target detection, named least absolute shrinkage and selection operator (LASSO)-based BS (LBS).

Interestingly, by using a linear regression model with L1 regularization (LASSO model),LBS transforms the discrete BS problem into the continuous optimization problem, which cannot only avoid the complicated subset selectionprocess but also evaluate the importance of all the bands simultaneously. The experiments on real hyperspectral data demonstrate that LBS is a very effective BS method for target detection.