A band selection approach based on a modified gray wolf optimizer and weight updating of bands for hyperspectral image

2021 ◽  
pp. 107805
Author(s):  
Mingwei Wang ◽  
Wei Liu ◽  
Maolin Chen ◽  
Xiaohui Huang ◽  
Wei Han
Keyword(s):  
Hyperspectral Image ◽  
Band Selection ◽  
Gray Wolf ◽  
Gray Wolf Optimizer ◽  
Selection Approach

scholarly journals A modified Cuckoo Search algorithm based optimal band subset selection approach for hyperspectral image classification

2020 ◽  
Author(s):  
Shrutika Sawant ◽  
Manoharan Prabukumar ◽  
Sathishkumar Samiappan
Keyword(s):  
Hyperspectral Image ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Local Optimum ◽  
Gray Wolf ◽  
Selection Approach ◽  
Ground Object

Band selection is an effective way to reduce the size of hyperspectral data and to overcome the “curse of dimensionality” in ground object classification. This paper presents a band selection approach based on modified Cuckoo Search (CS) optimisation with correlation-based initialisation. CS is a popular metaheuristic algorithm with efficient optimisation capabilities for band selection. However, it can easily fall into local optimum solutions. To avoid falling into a local optimum, an initialisation strategy based on correlation is adopted instead of random initialisation to initiate the location of nests. Experimental results with Indian Pines, Salinas and Pavia University datasets show that the proposed approach obtains overall accuracy of 82.83 %, 94.83 % and 91.79 %, respectively, which is higher than the original CS algorithm, Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Gray Wolf Optimisation (GWO).

A band selection approach based on Lévy sine cosine algorithm and alternative distribution for hyperspectral image

2020 ◽  
Vol 41 (9) ◽  
pp. 3429-3445 ◽  
Author(s):  
Mingwei Wang ◽  
Chunming Wu ◽  
Maolin Chen ◽  
Beiqing Chen ◽  
Ying Jiang
Keyword(s):  
Hyperspectral Image ◽  
Band Selection ◽  
Alternative Distribution ◽  
Selection Approach ◽  
Sine Cosine Algorithm

Gray Wolf Optimizer for hyperspectral band selection

2016 ◽  
Vol 40 ◽  
pp. 178-186 ◽  
Author(s):  
S.A. Medjahed ◽  
T. Ait Saadi ◽  
A. Benyettou ◽  
M. Ouali
Keyword(s):  
Band Selection ◽  
Gray Wolf ◽  
Gray Wolf Optimizer

scholarly journals Representative Band Selection for Hyperspectral Image Classification

2018 ◽  
Vol 7 (9) ◽  
pp. 338 ◽  
Author(s):  
Fuding Xie ◽  
Fangfei Li ◽  
Cunkuan Lei ◽  
Lina Ke
Keyword(s):  
Hyperspectral Image ◽  
Information Gain ◽  
Great Difficulty ◽  
Band Selection ◽  
Selection Strategy ◽  
Support Vector ◽  
Gray Wolf ◽  
Optimization Strategy ◽  
Band Combination ◽  
Good Classification

The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands and maintain a good classification ability. In this study, a novel hybrid filter-wrapper band selection method is proposed by a three-step strategy, i.e., band subset decomposition, band selection and band optimization. Based on the information gain (IG) and the spectral curve of the hyperspectral dataset, the band subset decomposition technique is improved, and a random selection strategy is suggested. The implementation of the first two steps addresses the problem of reducing inter-band redundancy. An optimization strategy based on a gray wolf optimizer (GWO) ensures that the selected band combination has a good classification ability. The classification performance of the selected band combination is verified on the Indian Pines, Pavia University and Salinas hyperspectral datasets with the aid of support vector machine (SVM) with a five-fold cross-validation. By comparing the proposed IG-GWO method with five state-of-the-art band selection approaches, the superiority of the proposed method for HSIs classification is experimentally demonstrated on three well-known hyperspectral datasets.

A Geometry-Based Band Selection Approach for Hyperspectral Image Analysis

2018 ◽  
Vol 56 (8) ◽  
pp. 4318-4333 ◽  
Author(s):  
Wenqiang Zhang ◽  
Xiaorun Li ◽  
Yaxing Dou ◽  
Liaoying Zhao
Keyword(s):  
Image Analysis ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Hyperspectral Image Analysis ◽  
Selection Approach
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