An improved endmember extraction method of mathematical morphology based on PPI algorithm

doi:10.11947/j.AGCS.2019.20180475

Abstract

Abstract: Automated morphological endmember extraction(AMEE) algorithm defines the spectral angular distance between the purest pixel and the most mixed pixel in the structural element as the morphological eccentricity index(MEI) to quantitatively denote the purity of the pixel. However, the most mixed pixels as the reference standard are not the same in different structural elements, especially when the pure pixels account for the majority of the structural elements, the mean spectrum of all the pixels will be closer to the pure pixels. At this time, the higher the MEI, the lower the purity of the pixel. To solve this problem, a novel endmember extraction algorithm is proposed in this paper which combines the pixel purity index (PPI) algorithm with AMEE algorithm and is named PPI-AMEE. In the structural element, the PPI is used to replace the MEI index in the AMEE algorithm to find the purest pixel. When the structural element is transformed, only the purest pixel can always be projected to the two ends of the randomly generated line, therefore the PPI value of the purest pixel will increase continuously, while the PPI value of the other pixels will not increase continuously. The PPI value of each pixel is accumulated and recorded until the iterative termination condition is satisfied, and a PPI image is finally obtained. The endmembers are selected from the pixels with higher PPI value. The PPI-AMEE algorithm runs the PPI algorithm in relatively small structural elements, and then processes the whole image with the expansion operation of mathematical morphology, which takes into account both the spectral and spatial information of the image. In the experiment, AVIRIS hyperspectral data from Cuprite area, Nevada, USA are used to validate the proposed PPI-AMEE algorithm. The experimental results show that the endmember extraction accuracy of PPI-AMEE algorithm is better than that of AMEE algorithm and PPI algorithm on the whole.

Key words: hyperspectral image, endmember extraction, pure pixel index, mathematical morphology

CLC Number:

TP391
P237

XU Jun, WANG Cailing, WANG Li. An improved endmember extraction method of mathematical morphology based on PPI algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8): 996-1003.

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