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

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 59; no. 3; pp. 2352 - 2364
Main Authors: Tong, Lyuyang, Du, Bo, Liu, Rong, Zhang, Liangpei, Tan, Kay Chen
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
(<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">μ + <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">λ ) selection strategy
Algorithms
Crossovers
Endmember extraction (EE)
Evolutionary algorithms
Evolutionary computation
Genetic crosses
Hyperspectral imaging
Indexes
multiobjective differential evolution (DE)
Multiple objective analysis
Mutants
Mutation
Offspring
Operators (mathematics)
Optimization
Ranking
ranking multiple mutations
Root-mean-square errors
Scaling
Scaling factors
Sociology
Sorting
Statistics
Vectors
ISSN:0196-2892, 1558-0644
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula> multiobjective differential evolution algorithm (<inline-formula> <tex-math notation="LaTeX">({\mu +\lambda }) </tex-math></inline-formula>-MODE) based on ranking multiple mutations. In the <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE to take advantage of the good information of the population. In addition, <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula> selection strategy. Eventually, experimental comparative results in three real HSIs reveal that our proposed <inline-formula> <tex-math notation="LaTeX">({\mu + \lambda }) </tex-math></inline-formula>-MODE is superior to other EE methods.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3004307