Band selection for hyperspectral images using probabilistic memetic algorithm

Feng, Liang; Tan, Ah-Hwee; Lim, Meng‐Hiot; Jiang, Si

doi:10.1007/s00500-014-1508-1

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…Band selection for hyperspectral image is the process to reduce the band size and identify the most informative bands or further analysis on the hyperspectral image data. Like the feature selection problem, band selection is NP-hard with only explication enumeration approaches known to solve it (Feng et al 2014). Bands selection techniques generally involve both searching algorithm and defining the bands range.…”

Section: Introductionmentioning

confidence: 99%

Optimal band selection for hyperspectral data with improved differential evolution

Wang

2015

J Ambient Intell Human Comput

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The optimal band selection has been a hot research topic and one of the difficult problems in the field of hyperspectral remote sensing. To address the issue of hyperspectral data and improve the data processing speed, we propose a new method to improve differential evolution algorithm. We use the ENVI software on the pretreatment of original spectral data and dividing subspace. The experiments are also conducted with the improved DE algorithm. In experiments, different evaluation criteria of band selection are taken as the fitness function value of the DE algorithm. By calculating the optimal band combination in different dimensions of size, we can obtain the optimal selection band of hyperspectral data in this paper. Experiments show that the improved differential evolution algorithm for hyperspectral data dimensionality reduction model not only obtains the optimal band combination, but also greatly improves the classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Optimal band selection for hyperspectral data with improved differential evolution

Wang

2015

J Ambient Intell Human Comput

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“…In recent years, because of its good performance on the preservation of primitive physical interpretability [11], feature selection has received increasing attention from researchers in the field of remote sensing. This type of method tries to pick the most representative subset from a large number of HSI features to maintain acceptable classification accuracy [12]. The general process of feature selection consists of the following basic steps: the generation procedure, evaluation function, stopping criterion comparison, and validation procedure [13].…”

Section: Introductionmentioning

confidence: 99%

“…Optimal feature subset selection for HSI is a typical NPhard problem [12], which should be solved with a proper search procedure [21]. Thus, swarm intelligence algorithms, such as ACA, GA, PSO, and CSA, have been widely employed in the optimized feature selection of the original hyperspectral data [22].…”

Section: Introductionmentioning

confidence: 99%

Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

Ding

et al. 2020

Open Geosciences

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Swarm intelligence algorithms have been widely used in the dimensional reduction of hyperspectral remote sensing imagery. The ant colony algorithm (ACA), the clone selection algorithm (CSA), particle swarm optimization (PSO), and the genetic algorithm (GA) are the most representative swarm intelligence algorithms and have often been used as subset generation procedures in the selection of optimal band subsets. However, studies on their comparative performance for band selection have been rare. For this paper, we employed ACA, CSA, PSO, GA, and a typical greedy algorithm (namely, sequential floating forward selection (SFFS)) as subset generation procedures and used the average Jeffreys–Matusita distance (JM) as the objective function. In this way, the band selection algorithm based on ACA (BS-ACA), band selection algorithm based on CSA (BS-CSA), band selection algorithm based on PSO (BS-PSO), band selection algorithm based on GA (BS-GA), and band selection algorithm based on SFFS (BS-SFFS) were tested and evaluated using two public datasets (the Indian Pines and Pavia University datasets). To evaluate the algorithms’ performance, the overall classification accuracy of maximum likelihood classifier and the average runtimes were calculated for band subsets of different sizes and were compared. The results show that the band subset selected by BS-PSO provides higher overall classification accuracy than the others and that its runtime is approximately equal to BS-GA’s, higher than those of BS-ACA, BS-CSA, and BS-SFFS. However, the premature characteristic of BS-ACA makes it unacceptable, and its average JM is lower than those of other algorithms. Furthermore, BS-PSO converged in 500 generations, whereas the other three swarm-intelligence based algorithms either ran into local optima or took more than 500 generations to converge. BS-PSO was thus proved to be an excellent band selection method for a hyperspectral image.

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“…selecting the optimized remote sensing bands), capable of reserving the primitive physical significance, has received an increasing attention among remote sensing researchers. Yet, it is a typical NP-hard problem [7] to choose the optimized bands of hyperspectral imagery, which can only be effectively solved with a reasonable objective function and a proper searching algorithm. The determination of objective function is usually application-specific, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Polymorphic Ant Colony Algorithm (PACA) for the Selection of Optimized Band Selection of Hyperspectral Remote Sensing Image

Ding¹,

Zhang

Li³

2016

Proceedings of the 2016 International Conference on Engineering and Technology Innovations

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With the definite labeled samples, it is difficult that avoid the curse of the dimensionality for the accurate and efficient classification of hyperspectral images. It is necessary that reduce the dimensionality of hyperspectral images. Therefore, Polymorphic Ant Colony Algorithm (PACA) based band selection algorithm (PACA-BS) for hyperspectral images is proposed in this paper. Compared with the common Ant Colony Algorithm (ACA) based band selection algorithm (ACA-BS), PACA-BS can significantly decrease the searching space and thus the time complexity. These algorithms are applied to select the bands of Hyperion and AVIRIS hyperspectral image according to the class separability criterion. Performance evaluation of algorithms is focused on the following aspects: computing time and overall classification accuracy. The results showed that the computing time of PACA-BS was markedly lower than ACA-BS. Furthermore, band sets of PACA-BS generate a higher overall classification accuracy. The PACA-BS is thus proved to be a promising and optimized method for band selection of hyperspectral image.

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Band selection for hyperspectral images using probabilistic memetic algorithm

Cited by 20 publications

References 24 publications

Optimal band selection for hyperspectral data with improved differential evolution

Optimal band selection for hyperspectral data with improved differential evolution

Comparison of swarm intelligence algorithms for optimized band selection of hyperspectral remote sensing image

A Polymorphic Ant Colony Algorithm (PACA) for the Selection of Optimized Band Selection of Hyperspectral Remote Sensing Image

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