EEA-PSO: Endmember extraction using advance particle swarm optimization

Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.

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“…e j α ij ,i¼ 1, 2, …,n. The root mean square error (rmse) between an original image and remixed image is calculated as [29],…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…In case of discrete feasible solution space D-PSO is improved to search particles in it on the basis of PSO. Moreover, the mapping relationship in the image and the feasible solution space can be given as [29],…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Hyperspectral Endmember Extraction Techniques

Kale¹,

Solankar²,

Nalawade³

2020

Processing and Analysis of Hyperspectral Data

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“…Besides, the conventional objective function, the volume of the simplex composed of extracted endmembers, was also used in the single-objective evolutionary algorithms [40]. In the past decade, miscellaneous algorithms have been designed either to improve the efficiency or enhance the optimization results of single-objective EE [41][42][43][44][45][46][47][48][49][50][51], including a variety of evolutionary algorithms, such as PSO algorithms [32,34,[40][41][42], ACO algorithms [33,[43][44][45], genetic algorithms (GAs) [46][47], and differential evolution (DE) algorithm [48].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Evolutionary Algorithm Based on Subspace Specified Searching for Hyperspectral Endmember Extraction

Lei,

Liu,

Tian

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, the introduction of multi-objective evolutionary algorithms (MOEAs) into the field of endmember extraction in hyperspectral unmixing has demonstrated a breadth of results that surpass those derived from single objective-based methodologies. Despite these advancements, the adaptation of MOEAs to endmember extraction, and the attainment of globally optimal solutions, represent unresolved challenges meriting continued exploration. This study addresses two principal obstacles in MOEA-based endmember extraction: the notorious "curse of dimensionality" in high-dimensional optimization, and the difficulty in striking a balance between convergence and population diversity. We propose a two-stage, evolutionary-based endmember extraction algorithm, referred to as TSEA, designed to confront these issues. A novel solution space splitting (3S) strategy is incorporated into TSEA that efficiently mitigates the curse of dimensionality by strategically contracting the search space. This advantage is largely attributed to the significant reduction of invalid solutions achieved through the simple application of a clustering procedure. Furthermore, a two-stage optimization approach is employed to meticulously uphold the convergence and diversity of the population, aiming to attain the optimal solution within the realm of highdimensional optimization. Empirical evidence from four real hyperspectral images demonstrates that the proposed TSEA outperforms other comparison multi-objective optimization algorithms. Thus, this study contributes to the ongoing discourse on the optimization and applicability of MOEAs in the context of endmember extraction.

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