Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm

• Published in: IEEE transactions on geoscience and remote sensing Vol. 49; no. 7; pp. 2635 - 2646
• Main Authors: Zhang, Bing, Sun, Xun, Gao, Lianru, Yang, Lina
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2011; Institute of Electrical and Electronics Engineers
• Subjects: Algorithm design and analysis; Ant colony optimization (ACO); Applied geophysics; Data mining; Earth sciences; Earth, ocean, space; endmember extraction; Exact sciences and technology; Feature extraction; Hyperspectral imaging; hyperspectral remote sensing; Internal geophysics; mixed pixel; Pixel; experimental studies; algorithms; models; hyperspectral remote sensing; simulation; data processing; remote sensing; decomposition; extraction; mixed pixel; solution; optimization; algorithm performance; endmember extraction; Ant colony optimization (ACO); errors; Pixel
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2011.2108305

Spectral mixture analysis has been an important research topic in remote sensing applications, particularly for hyperspectral remote sensing data processing. On the basis of linear spectral mixture models, this paper applied directed and weighted graphs to describe the relationship between pixels. In particular, we transformed the endmember extraction problem in the decomposition of mixed pixels into an issue of optimization and built feasible solution space to evaluate the practical significance of the objective function, thereby establishing two ant colony optimization algorithms for endmember extraction. In addition to the detailed process of calculation, we also addressed the effects of different operating parameters on algorithm performance. Finally we designed two sets of simulation data experiments and one set of actual data experiments, and the results of those experiments prove that endmember extraction based on ant colony algorithms can avoid some defects of N-FINDR, VCA and other algorithms, improve the representation of endmembers for all image pixels, decrease the average value of root-mean-square error, and therefore achieve better endmember extraction results than the N-FINDR and VCA algorithms.