A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification

Wu, Hao; Zhu, Jiali; Li, Shijin; Wan, Dingsheng; Lin, Lin

doi:10.1007/978-3-642-12990-2_37

Cited by 8 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In many real-world applications such as bioinformatics (Armananzas et al 2011;García-Torres et al 2013;Akand, Bain, andTemple 2010), medicine (da Silva et al 2011), text mining (Azam and Yao 2012;Feng et al 2012;Meng, Lin, and Yu 2011;Pinheiro et al 2012;Uguz 2011;Imani, Keyvanpour, and Azmi 2013), image processing (Jia et al 2013;Rashedi, Nezamabadi-pour, and Saryazdi 2013;Vignolo, Milone, and Scharcanski 2013) remote sensing (Ghosh, Datta, and Ghosh et al 2013;Guo et al 2008;Li et al 2011) and other domains (Pérez-Benitez and Padovese 2011; Wu et al 2010;Zhang et al 2011;Waad, Ghazi, and Mohamed 2013), the dimensionality of data are so high that they may lead to the breakdown of an ordinary feature selection algorithm. High dimensionality of the data asks for the development of more complicated methods to apply feature selection on a large number of features.…”

Section: Related Workmentioning

confidence: 98%

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Moshki

Kabiri

Mohebalhojeh

2015

Applied Artificial Intelligence

View full text Add to dashboard Cite

Feature selection in high-dimensional data is one of the active areas of research in pattern recognition. Most of the algorithms in this area try to select a subset of features in a way to maximize the accuracy of classification regardless of the number of selected features that affect classification time. In this article, a new method for feature selection algorithm in high-dimensional data is proposed that can control the trade-off between accuracy and classification time. This method is based on a greedy metaheuristic algorithm called greedy randomized adaptive search procedure (GRASP). It uses an extended version of a simulated annealing (SA) algorithm for local search.In this version of SA, new parameters are embedded that allow the algorithm to control the trade-off between accuracy and classification time. Experimental results show supremacy of the proposed method over previous versions of GRASP for feature selection. Also, they show how the trade-off between accuracy and classification time is controllable by the parameters introduced in the proposed method.

show abstract

Section: Related Workmentioning

confidence: 98%

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Moshki

Kabiri

Mohebalhojeh

2015

Applied Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Wrapper approaches use the classifier performance as the objective function for optimizing the subset of HSI bands [14], [15]. These methods encompass various heuristics and meta-heuristics, including biologically-inspired techniques [76]- [79], gravitational searches [80], and artificial immune systems [81]. Cao et al proposed a semisupervised approach in which their exploited the edge preserved filtering to improve the pixel-wised classification maps (and to assess the quality of the selected band subsets) [82].…”

Section: Related Literaturementioning

confidence: 99%

Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Hyperspectral imaging has become a mature technology which brings exciting possibilities in various domains, including satellite image analysis. However, the high dimensionality and volume of such imagery is a serious problem which needs to be faced in Earth Observation applications, where efficient acquisition, transfer and storage of hyperspectral images are key factors. To reduce the time (and ultimately cost) of transferring hyperspectral data from a satellite back to Earth, various band selection algorithms have been proposed. They are built upon the observation that for a vast number of applications only a subset of all bands convey the important information about the underlying material, hence we can safely decrease the data dimensionality without deteriorating the performance of hyperspectral classification and segmentation techniques. In this paper, we introduce a novel algorithm for hyperspectral band selection that couples new attention-based convolutional neural networks used to weight the bands according to their importance with an anomaly detection technique which is exploited for selecting the most important bands. The proposed attention-based approach is data-driven, re-uses convolutional activations at different depths of a deep architecture, identifying the most informative regions of the spectrum. Also, it is modular, easy to implement, seamlessly applicable to any convolutional network, and can be trained end-to-end using gradient descent. Our rigorous experiments, performed over benchmark sets and backed up with statistical tests, showed that the deep models equipped with the attention mechanism are competitive with the stateof-the-art band selection techniques and can work orders or magnitude faster, they deliver high-quality classification, and consistently identify significant bands in the training data, permitting the creation of refined and extremely compact sets that retain the most meaningful features. Also, the attention modules do not deteriorate the classification abilities, and slow down neither training nor inference of the deep models.

show abstract

“…At the same time, these images are usually composed of tens of or hundreds of spectral bands with high redundancy and great amount of computation in perspectral image classification. Therefore the most important and urgent issue is how to reduce the number of those bands largely with little loss of information or classification accuracy (Wu et al 2010). 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.…”

Section: Introductionmentioning

confidence: 99%

Optimal band selection for hyperspectral data with improved differential evolution

Wang

2015

J Ambient Intell Human Comput

View full text Add to dashboard Cite

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.

show abstract

A Hybrid Evolutionary Approach to Band Selection for Hyperspectral Image Classification

Cited by 8 publications

References 14 publications

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Scalable Feature Selection in High-Dimensional Data Based on GRASP

Hyperspectral Band Selection Using Attention-Based Convolutional Neural Networks

Optimal band selection for hyperspectral data with improved differential evolution

Contact Info

Product

Resources

About