Results 221 to 230 of about 9,721 (245)

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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An Unsupervised Band Selection Based on Band Similarity for Hyperspectral Image Target Detection

Proceedings of International Conference on Internet Multimedia Computing and Service, 2014
In 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, Jing Zhang 0023, Li Zhuo 0001, Chao Wang 0017, Qianlan Zhou   +4 more
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Unsupervised hyperspectral band selection with deep autoencoder unmixing

International Journal of Image and Data Fusion, 2021
Hyperspectral 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, Marwa S. Mostafa, Hala M. Ebeid, Mohamed Tolba   +3 more
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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 K. Schueller, Paul D. Gader, Young Ki Hong, Sangcheol Kim   +5 more
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Comparison of Unsupervised Band Selection Methods for Hyperspectral Imaging

2007
Different methods have been proposed in order to deal with the huge amount of information that hyperspectral applications involve. This paper presents a comparison of some of the methods proposed for band selection. A relevant and recent set of methods have been selected that cover the main tendencies in this field.
Adolfo Martínez Usó, Filiberto Pla, José Martínez Sotoca, Pedro García-Sevilla   +3 more
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Ant colony optimization for supervised and unsupervised hyperspectral band selection

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013
In this paper, ant colony optimization (ACO) is applied to hyperspectral band selection. The objective is to select a small band subset such that classification accuracy can be maintained or even improved. The ACO-based band selection technique in this research is independent of any classifier, resulting in lower computational cost.
Jianwei Gao, Qian Du 0001, Lianru Gao, Xu Sun 0005, Yuanfeng Wu, Bing Zhang 0001   +5 more
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Unsupervised Hyperspectral Band Selection Based on Spectral Rhythm Analysis

2014 27th SIBGRAPI Conference on Graphics, Patterns and Images, 2014
Remote sensing image classification aims to automatically categorize a monitored area in land cover classes. Hyperspectral images, which provide plenty of spectral information per pixel, allow achieving good accuracy results in classification problems.
Lilian Chaves Brandao dos Santos, Silvio Jamil Ferzoli Guimarães, Arnaldo de Albuquerque Araújo, Jefersson Alex dos Santos   +3 more
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Comparison of traditional and recent unsupervised band selection approaches in hyperspectral images

2016 24th Signal Processing and Communication Application Conference (SIU), 2016
In this paper, well-known traditional band selection methods which are used in hyperspectral imaging, namely, Maximum-Variance Principal Component Analysis (MVPCA), Maximum-SNR Principal Component Analysis (MSNRPCA), k-means, k-medoids, and recently proposed Automatic Band Selection (ABS) and Band Column Selection (BCS) approaches are compared.
Ali Can Karaca, Mehmet Kemal Güllü
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A novel unsupervised bands selection algorithm for hyperspectral image

Optik, 2018
Abstract A novel bands selection method based on ABS (Adaptive Band Selection) and JSKF (Joint Skewness-Kurtosis Figure) is proposed in this paper. The hyperspectral data is separated into different sub-spaces by employing ABS and JSKF respectively. Subsequently a novel optimal bands selection method NIA (Normalization Index Algorithm) is proposed to
Xiaoping Du, Hang Chen, Zhengjun Liu, Chengwei Yang   +3 more
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Determining the dimensionality of hyperspectral imagery for unsupervised band selection

SPIE Proceedings, 2003
This paper addresses the problem of estimating the dimension of a hyperspectral image. Spanning and intrinsic dimension concepts are studied as ways to determine the number of degrees of freedom needed to represent a Hyperspectral Image. Algorithms for the estimation of spanning and intrinsic dimension are reviewed and applied to hyperspectral images ...
Alejandra Umana-Diaz, Miguel Velez-Reyes
openaire   +1 more source