Results 141 to 150 of about 53,858 (186)

Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection

IEEE Transactions on Image Processing, 2017
Band 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, Zheng, Xiangtao, Lu, Xiaoqiang, Yuan, Y (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China.   +3 more
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Marginalized Graph Self-Representation for Unsupervised Hyperspectral Band Selection

IEEE Transactions on Geoscience and Remote Sensing, 2022
Unsupervised band selection is an essential step in preprocessing hyperspectral images (HSIs) to select informative bands. Most existing methods exploit the spatial information from the entire HSI while ignoring the difference between diverse homogeneous regions. Moreover, traditional methods utilize the limited size of data for model training that may
Yongshan Zhang, Xinxin Wang, Xinwei Jiang, Yicong Zhou   +3 more
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Unsupervised Hyperspectral Image Band Selection via Column Subset Selection

IEEE Geoscience and Remote Sensing Letters, 2015
In this letter, we proposed a novel band selection algorithm for hyperspectral images (HSIs) based on column subset selection. The main idea of the proposed algorithm comes from the column subset selection problem in numerical linear algebra. It selects a group of bands, which maximizes the volume of the selected subset of columns.
null Chi Wang, null Maoguo Gong, null Mingyang Zhang, null Yongqiang Chan   +3 more
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Efficient Unsupervised Band Selection Through Spectral Rhythms

IEEE Journal of Selected Topics in Signal Processing, 2015
The main goal of remote sensing image classification is to associate land cover classes to each pixel in the monitored area. In this sense, hyperspectral images play a key role by providing detailed spectral information per pixel. On the other hand, although the huge amount of spectral bands enables the creation of more accurate thematic maps, they can
Lilian Chaves B. dos Santos, Silvio Jamil F. Guimaraes, Jefersson A. dos Santos   +2 more
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Unsupervised Hyperspectral Band Selection Based on Hypergraph Spectral Clustering

IEEE Geoscience and Remote Sensing Letters, 2022
Hyperspectral images can provide spectral characteristics related to the physical properties of different materials, which arouses great interest in many fields. Band selection (BS) could effectively solve the problem of high dimensions and redundant information of HSI data.
Jingyu Wang, Hongmei Wang, Zhenyu Ma, Lin Wang, Qi Wang, Xuelong Li   +5 more
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Unsupervised band selection for hyperspectral image analysis

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
Band 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 ...
null Qian Du, null He Yang
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Unsupervised Hyperspectral Band Selection by Sequential Clustering

Proceedings of the International Conference on Watermarking and Image Processing, 2017
Hyperspectral data provide detailed information about the spectral properties of an observed scene. Although hyperspectral images contain much information, the reduction of dimensionality of these data is sometimes necessary to minimize their processing complexity. Band selection techniques are ways to perform dimensionality reduction. These techniques
Mohammed Bilel Amri, Sarra Ikram Benabadji, Moussa Sofiane Karoui   +2 more
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Unsupervised Hyperspectral Band Selection by Dominant Set Extraction

IEEE Transactions on Geoscience and Remote Sensing, 2016
Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands.
Guokang Zhu, Yuancheng Huang, Jingsheng Lei, Zhongqin Bi, Feifei Xu   +4 more
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Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis

IEEE Geoscience and Remote Sensing Letters, 2008
Band 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, He Yang
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Unsupervised hyperspectral band selection for apple Marssonina blotch detection

Computers and Electronics in Agriculture, 2018
Abstract Apple Marssonina blotch (AMB) is a severe fungal disease that has been plaguing top apple producing countries in the world since it was first found in Japan in 1907. The disease causes premature defoliation and eventually leads to fruit shrinkage and reduction of starch content.
Mubarakat Shuaibu, Won Suk Lee, John Schueller, Paul Gader, Young Ki Hong, Sangcheol Kim   +5 more
openaire   +1 more source