Semi-Supervised Hyperspectral Band Selection Based on Dynamic Classifier Selection

Cao, Xin; Wei, Cuicui; Feng, Jie; Zhao, Jing; Jiao, Licheng

doi:10.1109/jstars.2019.2899157

Cited by 33 publications

(12 citation statements)

References 45 publications

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“…In order to validate the superiority of our proposed RMGF, five other unsupervised hyperspectral band selection methods are used for comparison, they are as follows: 1) UBS [Chang and Wang, 2006], which divides the hyperspectral image cube into multiple subcubes at equal width based on the required number of selected bands, each segmentation point is viewed as the representative band. 2) TOF [Wang et al, 2018], which aims to construct an optimal clustering model with rank constraint to provide an effective criterion for selecting bands on existing clustering structure.…”

Section: Compared Methodsmentioning

confidence: 99%

“…Based on the availability of sample labels, existing band selection methods can be categorized into supervised ones [Feng et al, 2014;Cao et al, 2019] and unsupervised ones [Jia et al, 2012;Yuan et al, 2016]. For supervised methods, they need sample labels to train a classifier to select the most optimal bands.…”

Section: Introductionmentioning

confidence: 99%

“…Rankingbased methods consists of two steps, i.e., evaluating the importance of each band and than select the most important bands. Typical evaluation criteria include information entropy [Guo et al, 2006], information divergence [Chang and Wang, 2006] and maximum variance [Chang et al, 1999]. As a kind of typical unsupervised leaning, clustering is widely used for hyperspectral band selection in the past few years and obtain satisfying results [Yuan et al, 2015;Zhang et al, 2017;Wang et al, 2018;2020a].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Tang

Liu

Zhu

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.

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Section: Compared Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Tang

Liu

Zhu

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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“…As for wrapper methods, the feature selection and classification are separated, and conducted iteratively. Typical examples are dynamic classifier [12], hybrid whale optimization algorithm with simulated annealing (WOASA) [13], modified ant lion optimizer (MALO) [14]. In the embedded methods, the feature selection and classification are unified into one model.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are some HSIs which lack labeled samples, because labeling samples is a costly, time consuming, and labor-intensive task. On these HSIs with a small number of labeled samples, unsupervised learning and semi-supervised learning have played huge roles [12], [18], [27]. As the number of HSIs increases, it can be found that many HSIs are related.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Cross-Scene Hyperspectral Image Classification Using Cross-Domain I-ReliefF

Zhang

Ling

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In the classification of hyperspectral images (HSIs), too many spectral bands (features) cause feature redundancy, resulting in a reduction in classification accuracy. In order to solve this problem, it is a good method to use feature selection to search for a feature subset which is useful for classification. Iterative ReliefF (I-ReliefF) is a traditional single-scene-based algorithm, it has good convergence, efficiency, and can handle feature selection problems well in most scenes. Most singlescene-based feature selection methods perform poorly in some scenes (domains) which lack labeled samples. As the number of HSIs increases, the cross-scene feature selection algorithms which utilize two scenes to deal with the high dimension and low sample size problem are more and more desired. The spectral shift is a common problem in cross-scene feature selection. It leads to difference in spectral feature distribution between source and target scenes even though these scenes are highly similar.To solve above problems, we extend I-ReliefF to a cross-scene algorithm: cross-domain I-ReliefF (CDIRF). CDIRF includes a cross-scene rule to update feature weights, which considers the separability of different land-cover classes and the consistency of the spectral features between two scenes. So CDIRF can effectively utilize the information of source scene to improve the performance of feature selection in target scene. The experiments are conducted on three cross-scene datasets for verification, and the experimental results demonstrate the superiority and feasibility of the proposed algorithm.Index Terms-Hyperspectral images; cross-scene feature selection; cross-domain I-ReliefF.

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Band selection strategies for hyperspectral image classification based on machine learning and artificial intelligent techniques –Survey

2021

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Semi-Supervised Hyperspectral Band Selection Based on Dynamic Classifier Selection

Cited by 33 publications

References 45 publications

Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering

Feature Selection for Cross-Scene Hyperspectral Image Classification Using Cross-Domain I-ReliefF

Band selection strategies for hyperspectral image classification based on machine learning and artificial intelligent techniques –Survey

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