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Detecting certain targets from hyperspectral images (HSIs) is of great interest for both civilian and military applications, with the aim being to detect and identify target pixels based on specific spectral signatures. However, the classical algorithms are generally dependent on the specific statistical hypothesis test, and the algorithms may only perform well with certain assumptions. Therefore,...
Sparse representation has achieved great success in the hyperspectral image processing field. However, with regard to target detection, the state-of-the-art sparsity-based algorithms are ad hoc and no different to a classifier. In this paper, a novel target detection algorithm is proposed, combining an elaborately designed sparsity model and the binary hypothesis statistics. With the strong similarity...
Traditional Orthogonal Subspace Projection (OSP) target detection method can not solve the problem of nonlinear mixing of endmember spectra. Meanwhile, Kernelized Orthogonal Subspace Projection (KOSP) method maps the inseparable data into high dimension space where the target endmembers and background endmembers can be separated. However, the background subspace remains the same for different pixels...
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