Hyperspectral Band Selection Based on Adaptive Neighborhood Grouping and Local Structure Correlation

Band selection is a direct and effective dimension reduction method and is one of the hotspots in hyperspectral remote sensing research. However, most of the methods ignore the orderliness and correlation of the selected bands and construct band subsets only according to the number of clustering centers desired by band sequencing. To address this issue, this article proposes a band selection method based on adaptive neighborhood grouping and local structure correlation (ANG-LSC). An adaptive subspace method is adopted to segment hyperspectral image cubes in space to avoid obtaining highly correlated subsets. Then, the product of local density and distance factor is utilized to sort each band and select the desired cluster center number. Finally, through the information entropy and correlation analysis of bands in different clusters, the most representative bands are selected from each cluster. Regarding evaluating the effectiveness of the proposed method, comparative experiments with the state-of-the-art methods are conducted on three public hyperspectral datasets. Experimental results demonstrate the superiority and robustness of ANG-LSC.

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An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

Remote Sensing ◽

10.3390/rs11030350 ◽

2019 ◽

Vol 11 (3) ◽

pp. 350 ◽

Cited By ~ 7

Author(s):

Qiang Li ◽

Qi Wang ◽

Xuelong Li

Keyword(s):

Nearest Neighbor ◽

Hyperspectral Image ◽

Local Density ◽

Computational Time ◽

Band Selection ◽

Data Sets ◽

Selection Methods ◽

Clustering Method ◽

Slope Change ◽

Shared Nearest Neighbor

A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.

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A survey of band selection techniques for hyperspectral image classification

Journal of Spectral Imaging ◽

10.1255/jsi.2020.a5 ◽

2020 ◽

Cited By ~ 3

Author(s):

Shrutika Sawant ◽

Manoharan Prabukumar

Keyword(s):

Image Classification ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Band Selection ◽

Hyperspectral Image Classification ◽

Spectral Bands ◽

Time Operation ◽

Ground Object ◽

Comprehensive Survey ◽

Highly Correlated

Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes. However, such high-dimensional data also contain highly correlated and irrelevant information, leading to the curse of dimensionality (also called the Hughes phenomenon). It is necessary to reduce these bands before further analysis, such as land cover classification and target detection. Band selection is an effective way to reduce the size of hyperspectral data and to overcome the curse of the dimensionality problem in ground object classification. Focusing on the classification task, this article provides an extensive and comprehensive survey on band selection techniques describing the categorisation of methods, methodology used, different searching approaches and various technical difficulties, as well as their performances. Our purpose is to highlight the progress attained in band selection techniques for hyperspectral image classification and to identify possible avenues for future work, in order to achieve better performance in real-time operation.

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A Joint Landscape Metric and Error Image Approach to Unsupervised Band Selection for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3072948 ◽

2021 ◽

pp. 1-5

Author(s):

Peichao Gao ◽

Hong Zhang ◽

Zhiwei Wu ◽

Jicheng Wang

Keyword(s):

Image Classification ◽

Hyperspectral Image ◽

Band Selection ◽

Hyperspectral Image Classification ◽

Landscape Metric ◽

Selection For

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Adaptive Neighborhood Strategy Based Generative Adversarial Network for Hyperspectral Image Classification

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324088 ◽

2020 ◽

Author(s):

Hongbo Liang ◽

Wenxing Bao ◽

Bingbing Lei ◽

Jian Zhang ◽

Kewen Qu

Keyword(s):

Image Classification ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adaptive Neighborhood

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Classification of SSVEP-based BCIs using Genetic Algorithm

Journal Of Big Data ◽

10.1186/s40537-021-00478-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hamideh Soltani ◽

Zahra Einalou ◽

Mehrdad Dadgostar ◽

Keivan Maghooli

Keyword(s):

Genetic Algorithm ◽

Dimension Reduction ◽

Classification Accuracy ◽

Bayesian Method ◽

Computer Interface ◽

Support Vector ◽

Svm Classifier ◽

Effective Dimension ◽

Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

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