Abstract
Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, where several different optimization objectives have been proposed from different perspectives. In all of these methods, only one objective function has to be optimized, which represents a specific characteristic of endmembers. However, one single-objective function may not be able to express all the characteristics of endmembers from various aspects, which would not be powerful enough to provide satisfactory unmixing results because of the complexity of remote sensing images. In this paper, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is utilized to tackle the problem of EE, where two objective functions, namely, volume maximization (VM) and root-mean-square error (RMSE) minimization are simultaneously optimized. Experimental results on two real hyperspectral images show the superiority of the proposed MODPSO with respect to the single objective D-PSO method, and MODPSO still needs further improvement on the optimization of the VM with respect to other approaches.
Highlights
Each pixel of hyperspectral image (HSI) has tens or hundreds of values corresponding to its spectral bands, which can effectively represent the unique ground objects [1,2]
Experimental results of the Washington dataset showed that N-FINDR and vertex component analysis (VCA) failed to extract the fourth endmember, which resulted in a larger root-mean-square error (RMSE) than the other two methods, while the volumes obtained by N-FINDR and VCA were much larger than the other two methods
This paper proposed a multiobjective optimization method multiobjective discrete particle swarm optimization algorithm (MODPSO) for endmember extraction
Summary
Each pixel of hyperspectral image (HSI) has tens or hundreds of values corresponding to its spectral bands, which can effectively represent the unique ground objects [1,2]. Mixed pixels, constituting more than one distinct material, may widely exist in the HSI due to the limited spatial resolution, which makes one single pixel not pure and brings troubles to accurate precision analysis of HSIs [4,5,6]. Spectral unmixing (SU) is an effective technique to resolve the mixed pixels problem, which decomposes the mixed pixels into a collection of pure materials, named endmembers, as well as the corresponding abundances [7]. Abundance estimation is the process to estimate different proportion of each endmember in a mixed pixel.
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