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Hyperspectral Band Selection by Multitask Sparsity Pursuit
IEEE Transactions on Geoscience and Remote Sensing, 2015Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the ...
Yuan Yuan 0001 +2 more
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Representative band selection for hyperspectral image classification
Journal of Visual Communication and Image Representation, 2017Abstract High dimensional curse for hyperspectral images is one major challenge in image classification. In this work, we introduce a novel spectral band selection method by representative band mining. In the proposed method, the distance between two spectral bands is measured by using disjoint information.
Ronglu Yang +4 more
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Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection
IEEE Transactions on Image Processing, 2017Band selection, as a special case of the feature selection problem, tries to remove redundant bands and select a few important bands to represent the whole image cube. This has attracted much attention, since the selected bands provide discriminative information for further applications and reduce the computational burden.
Yuan Yuan, Xiaoqiang Lu, Xiangtao Zheng
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Constrained Band Subset Selection for Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2017This letter extends the constrained band selection (CBS) technique to constrained band subset selection (CBSS) in a similar manner that constrained energy minimization has been extended to linearly constrained minimum variance. CBSS constrains multiple bands as a band subset as opposed to CBS constraining a single band as a singleton set.
Lin Wang 0028 +3 more
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Hyperspectral Band Selection with Convolutional Neural Network
2018Band selection is a kind of dimension reduction method, which tries to remove redundant bands and choose several pivotal bands to represent the entire hyperspectral image (HSI). Supervised band selection algorithms tend to perform well because of the introduction of prior information.
Rui Cai 0002 +2 more
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Unsupervised band selection for hyperspectral image analysis
2007 IEEE International Geoscience and Remote Sensing Symposium, 2007Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that using a subset of original bands the accuracy is unchanged or tolerably degraded ...
Qian Du 0001, He Yang
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Hyperspectral band selection using firefly algorithm
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014A novel band selection algorithm for hyperspectral dimensionality reduction by improving the firefly algorithm is put forward. Specifically, the framework which using bio-inspired algorithm for hyperspectral band selection is described; the between-class separability criteria such as Jeffreys-Matusita (JM) distance, transformation divergence (TD) are ...
Hongjun Su, Qiannan Li, Peijun Du
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Hyperspectral Band Selection Based on Rough Set
IEEE Transactions on Geoscience and Remote Sensing, 2015Band selection is a well-known approach to reduce the dimensionality of hyperspectral imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of the hyperspectral imagery ...
Lorenzo Bruzzone, Swarnajyoti Patra
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Constrained multiple band selection for hyperspectral imagery
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016A recent developed band selection, called constrained band selection (CBS), makes use of constrained energy minimization (CEM) to constrain a single band to calculate its priority for band selection (BS). This paper extends such CEM-BS to a constrained multiple band selection (CMBS)-based method, to be called linearly constrained minimum variance ...
Hsiao-Chi Li +3 more
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An Efficient Method for Supervised Hyperspectral Band Selection
IEEE Geoscience and Remote Sensing Letters, 2011Band selection is often applied to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance.
He Yang +3 more
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