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Orthogonal Bases Approach for the Decomposition of Mixed Pixels in Hyperspectral Imagery

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3 Author(s)
Xuetao Tao ; Dept. of Electron. Eng., Fudan Univ., Shanghai ; Bin Wang ; Liming Zhang

The N-FINDR algorithm has been widely used in hyperspectral image analysis for endmember extraction due to its simplicity and effectiveness. However, there are several disadvantages of implementing the N-FINDR. This letter proposes an algorithm for decomposition of mixed pixels. It improves the N-FINDR in several aspects. First, an iterative Gram-Schmidt orthogonalization is applied in the endmember searching process to replace the matrix determinant calculation used in N-FINDR, which makes this algorithm run very fast and can also guarantee the stability of its final results. Second, with the set of orthogonal bases obtained by the Gram-Schmidt orthogonalization, the algorithm can also help to estimate the proper number of endmembers and unmix the original images by itself. In addition, unlike the N-FINDR, a dimensionality reduction transform is not necessary in this algorithm. Experimental results of both simulated images and practical remote sensing images demonstrate that this algorithm is a fast and accurate algorithm for the decomposition of mixed pixels.

Published in:

IEEE Geoscience and Remote Sensing Letters  (Volume:6 ,  Issue: 2 )