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Unsupervised Hyperspectral Band Selection Using Graphics Processing Units
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011 The high dimensionality of hyperspectral imagery challenges image processing and analysis. Band selection is a common technique for dimensionality reduction. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands.
Qian Du
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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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Unsupervised Hyperspectral Image Band Selection via Column Subset Selection
IEEE Geoscience and Remote Sensing Letters, 2015In 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.
Maoguo Gong, Mingyang Zhang, Chi Wang
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Unsupervised Hyperspectral Band Selection Based on Hypergraph Spectral Clustering
IEEE Geoscience and Remote Sensing Letters, 2022Hyperspectral 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 0002 +5 more
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Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification
IEEE Transactions on Image Processing, 2023With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose bands based on band correlation and information has become more significant, but also complicated. Band selection is a combinatorial optimization problem, and intelligent optimization algorithms have been shown to be crucial in solving combinatorial optimization ...
Bing Tu, Guoyun Zhang, Wu Meng
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Superpixel-Based Unsupervised Band Selection for Classification of Hyperspectral Images
IEEE Transactions on Geoscience and Remote Sensing, 2018This paper presents an unsupervised approach to band selection in hyperspectral images that considers both spectral and spatial information in data dimensionality reduction. The approach exploits the concepts of superpixel and chunklets for identifying the spectral channels most suitable to be used in classification for discriminating land-cover ...
Haishi Zhao +2 more
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Efficient Unsupervised Band Selection Through Spectral Rhythms
IEEE Journal of Selected Topics in Signal Processing, 2015The 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 Brandao dos Santos +2 more
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Unsupervised Hyperspectral Band Selection by Sequential Clustering
Proceedings of the International Conference on Watermarking and Image Processing, 2017Hyperspectral 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 +2 more
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An Unsupervised Band Selection Based on Band Similarity for Hyperspectral Image Target Detection
Proceedings of International Conference on Internet Multimedia Computing and Service, 2014In remote sensing data processing, band selection is very important for hyperspectral image processing and analysis, which utilize the most distinctive and informative band subset of original bands to reduce data dimensionality. Although band selection can significantly alleviate the computational burden, the process itself may cause additional ...
Yan Cao +4 more
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Unsupervised hyperspectral band selection with deep autoencoder unmixing
International Journal of Image and Data Fusion, 2021Hyperspectral imaging (HSI) is a beneficial source of information for numerous civil and military applications, but high dimensionality and strong correlation limits HSI classification performance....
Menna M. Elkholy +3 more
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