Hyperspectral Endmember Extraction by (μ + λ) Multiobjective Differential Evolution Algorithm Based on Ranking Multiple Mutations

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number9130923
Pages (from-to)2352-2364
Journal / PublicationIEEE Transactions on Geoscience and Remote Sensing
Volume59
Issue number3
Online published1 Jul 2020
Publication statusPublished - Mar 2021

Abstract

Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the multiobjective optimization problem to optimize the volume maximization (VM) and root-mean-square error (RMSE) simultaneously. However, it is often quite challenging to balance the conflict of these objectives. In order to tackle the challenges of multiobjective EE, we present a (μ + λ) multiobjective differential evolution algorithm ((μ + λ)-MODE) based on ranking multiple mutations. In the (μ + λ)-MODE algorithm, ranking multiple mutations are adopted to create the mutant vectors via the scaling factor pool to enhance the population diversity. Moreover, mutant vectors employ the binary crossover operator to generate the trial vectors through a crossover control parameter pool in (μ + λ)-MODE to take advantage of the good information of the population. In addition, (μ + λ)-MODE utilizes the fast nondominated sorting approach to sort the parent and trial vectors, and then selects the elitism offspring as the next population via the (μ + λ) selection strategy. Eventually, experimental comparative results in three real HSIs reveal that our proposed (μ + λ)-MODE is superior to other EE methods.

Research Area(s)

  • (μ + λ) selection strategy, Endmember extraction (EE), multiobjective differential evolution (DE), ranking multiple mutations

Citation Format(s)

Hyperspectral Endmember Extraction by (μ + λ) Multiobjective Differential Evolution Algorithm Based on Ranking Multiple Mutations. / Tong, Lyuyang; Du, Bo; Liu, Rong; Zhang, Liangpei; Tan, Kay Chen.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, No. 3, 9130923, 03.2021, p. 2352-2364.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review