Nature-Inspired Framework for Hyperspectral Band Selection

Nakamura, Ricardo; Fonseca, Leila María García; Santos, Jefersson A. dos; Torres, Ricardo da S.; Yang, Xin-She; Papa, João P.

doi:10.1109/tgrs.2013.2258351

Cited by 63 publications

(20 citation statements)

References 52 publications

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“…In recent years, various classification algorithms have been emerged, such as optimum-path forest classifier [9,10,11,12] and pose classifier [13,14]. They are widely used in image classification, search and rerank fields [15,16,17,18,19,20].…”

Section: Related Workmentioning

confidence: 99%

Robust face detection using local CNN and SVM based on kernel combination

et al. 2016

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Section: Related Workmentioning

confidence: 99%

Robust face detection using local CNN and SVM based on kernel combination

et al. 2016

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“…However, a representative spectral signature for each class can be used to compute the minimum spatial covariance [21]. Furthermore, sometimes the accuracy of a classifier can directly be used as objective function [22]. The core objective of any searching strategy such as forward searching methods [19] is to avoid testing all possible band combinations.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction

Alqarni

Khan

Hussain

et al. 2019

Optik

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Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhibited in the data but at the expense of loosing its original representation. To cope with the said issue, an unsupervised non-linear segmented and non-segmented stacked denoising autoencoder (UDAE) based BR method is proposed. Our aim is to find an optimal mapping and construct a lowerdimensional space that has a similar structure to the original data with least reconstruction error. The proposed method first confronts the original hyperspectral data into smaller regions in spatial domain and then each region is processed by UDAE individually. This results in reduced complexity and improved efficiency of BR for both semi-supervised and unsupervised tasks, i.e. classification and clustering. Our experiments on publicly available hyperspectral datasets with various types of classifiers demonstrate the effectiveness of UDAE method which equates favorably with other state-of-the-art dimensionality reduction and BR methods.

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“…Many of the feature extraction algorithm are constructed based on the geometric and affine transformations, such as Principal Component Analysis (PCA) [7], Maximum-Noise Fraction transformation (MNF) [8], Independent Component Analysis (ICA) [9], and wavelet-based transforms [10]. Although these aforementioned methods have been widely utilized in the data compression of HSIs, they may lead to physically non-interpretable results since they always realize the compression purpose by changing the original physical meaning of the original data [11].…”

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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Nature-Inspired Framework for Hyperspectral Band Selection

Cited by 63 publications

References 52 publications

Robust face detection using local CNN and SVM based on kernel combination

Robust face detection using local CNN and SVM based on kernel combination

Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction

Hyperspectral band selection using crossover‐based gravitational search algorithm

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