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The target-constrained interference-minimized filter (TCIMF) has been widely used in various target detection applications for hyperspectral data exploitation. However, like other classic target detection algorithms, the complex background (BKG) of a scene significantly impacts its performance. To better cope with BKG, this article develops a BKG-annihilated TCIMF (BA-TCIMF) that can be implemented in two stages with BKG annihilation in the first stage followed by target detectability (TD) enhancement and target BKG suppression performed by TCIMF in the second stage. In particular, the second stage extracts additional BKG signatures from the BA data as unwanted signatures to enhance TD via orthogonal subspace projection (OSP) while suppressing target BKG in the BA data by constrained energy minimization (CEM). Depending upon how these two stages are carried out, three versions of BA-TCIMF, data sphered BA-TCIMF (DS-BA-TCIMF), low-rank and sparse matrix decomposition (LRaSMD) BA-TCIMF (LRaSMD-BA-TCIMF), and component decomposition analysis-BA-TCIMF (CDA-BA-TCIMF), are derived. Experimental results demonstrate that BA-TCIMF performs as it is designed and better than many existing target detection algorithms.
Chen et al. (Sat,) studied this question.