Optimized Hyperspectral Band Selection Using Particle Swarm Optimization

Su, Hongjun; Du, Qian; Chen, Genshe; Du, Peijun

doi:10.1109/jstars.2014.2312539

Cited by 174 publications

(68 citation statements)

References 23 publications

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“…The 20 spectral features, 5 PCA components, 2 Gabor and 8 GLCM are stacked into a 35 feature vector. After extraction of feature spaces on HSI and LIDAR data, the proposed method applied an optimization method based on particle swarm optimization (PSO) to select optimum features (Su et al, 2014). The basic PSO algorithm starts with a population of random particles.…”

Section: Feature Extraction and Selection On Hsi And Lidar Datamentioning

confidence: 99%

Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system

Bigdeli

Samadzadegan

Reinartz

2015

International Journal of Applied Earth Observation and Geoinfor

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Section: Feature Extraction and Selection On Hsi And Lidar Datamentioning

confidence: 99%

Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system

Bigdeli

Samadzadegan

Reinartz

2015

International Journal of Applied Earth Observation and Geoinfor

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“…Most interestingly, a band group-wise method was developed [38], which used band combinations by compressive sensing and a multitask sparsity pursuit (MTSP)-based criterion to select band combinations based on linear sparse representation via an evolution-based algorithm-derived search strategy. Another SMMBS approach is to narrow the search range by specifying particular parameters to limit a small number of band subsets as candidate optimal sets, then follow an optimization algorithm such as PSO [35] or FA [36] to find an optimal band subset from the selected candidate set of band subsets.…”

Section: This Is Infeasible Ifmentioning

confidence: 99%

Band Subset Selection for Hyperspectral Image Classification

Song

Chang

2018

Remote Sensing

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This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV) derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ) and successive (SC) algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

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“…The obtained spectral data can characterise the properties of different materials and potentially be helpful for the analysis of different objects in the scene. However, due to many of the spectral bands are highly related, the hyperspectral images (HSIs) are always of high degree of information redundancy and requires a lot of storage space [1]. Although too few spectral bands are hard to produce acceptable accuracy, the serious information redundancy in HSIs also wrecks the data analysis accuracy, and causes the well-known Curse of Dimensionality or Hughes Phenomenon [2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Thereby, many natureinspired algorithms (NAs) have been introduced to reduce the computational time of band selection in recent years. For example, classical NAs including Genetic Algorithm (GA) [13], Particle Swarm Optimization (PSO) [1,14], and Ant Colonization Optimization (ACO) [15] etc. have been adapted to the area of band selection for HSIs.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral band selection using crossover‐based gravitational search algorithm

Zhang

Liu

et al. 2019

IET Image Processing

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Band selection is an important data dimensionality reduction tool in hyperspectral images (HSIs). To identify the most informative subset band from the hundreds of highly corrected bands in HSIs, a novel hyperspectral band selection method using a crossover based gravitational search algorithm (CGSA) is presented in this paper. In this method, the discriminative capability of each band subset is evaluated by a combined optimization criterion, which is constructed based on the overall classification accuracy and the size of the band subset. As the evolution of the criterion, the subset is updated using the V-shaped transfer function based CGSA. Ultimately, the band subset with the best fitness value is selected. Experiments on two public hyperspectral datasets, i.e. the Indian Pines dataset and the Pavia University dataset, have been conducted to test the performance of the proposed method. Comparing experimental results against the basic GSA and the PSOGSA (hybrid PSO and GSA) revealed that all of the three GSA variants can considerably reduce the band dimensionality of HSIs without damaging their classification accuracy. Moreover, the CGSA shows superiority on both the effectiveness and efficiency compared to the other two GSA variants.

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Optimized Hyperspectral Band Selection Using Particle Swarm Optimization

Cited by 174 publications

References 23 publications

Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system

Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system

Band Subset Selection for Hyperspectral Image Classification

Hyperspectral band selection using crossover‐based gravitational search algorithm

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