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This paper presents an adaptive matched subspace method for detecting sub-pixel targets in hyperspectral imagery based on fully constrained linear separation. This method aims to overcome the defects of the sub-pixel detecting methods based on linear mixture model. By means of this method, not only the abundance of targets in different pixels can be detected, but also the pixels containing targets can be separated from the other pixels reliably. In addition, cross correlation spectrum matching technique is applied to the method to compute the sorts of the endmembers in each pixel in the imagery. Then instead of choosing all the endmembers, we choose the according sorts of endmembers in the method. In this way, the separability between the targets and the other ground objects can be improved. The experiments show that no matter whether the number of the sorts of endmembers is overestimated or underestimated, the detecting results of the method presented in this paper are better than other traditional sub-pixel detecting methods based on linear-mixture model. And this method can formulate an effective rule to separate the targets and background with a better performance than the other methods. Besides, it also performs better as to the targets spectrally similar to the background objects and the targets with a small number.
This paper presents an adaptive matched subspace method for detecting sub-pixel targets in hyperspectral imagery based on fully constrained linear separation. This means aims to overcome the defects of the sub-pixel detecting methods based on linear mixture model. By means of this method, not only the abundance of targets in different pixels can be detected, but also the pixels containing targets can be separated from the other pixels reliably. In addition, cross correlation spectrum matching technique is applied to the method to compute the sorts of the endmembers in each pixel in the imagery. Then instead of choosing all the endmembers, we choose the according sorts of endmembers in the method. In this way, the separability between the targets and the other ground objects can be improved. The experiments show that no matter whether the number of the sorts of endmembers is overestimated or underestimated, the detecting results of the method presented in this paper are better than oth er traditional sub-pixel detecting methods based on linear-mixture model. And this method can formulate an effective rule to separate the targets and background with a better performance than the other methods. Besides, it also perform better to the targets spectrally similar to the background objects and the targets with a small number.