Results 211 to 220 of about 9,839 (245)

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 ...
Qian Du 0001, He Yang
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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 0003, Xinwei Jiang, Yicong Zhou   +3 more
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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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Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification

IEEE Transactions on Image Processing, 2023
With 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
exaly   +3 more sources

Superpixel-Based Unsupervised Band Selection for Classification of Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2018
This 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, Lorenzo Bruzzone, Renchu Guan   +2 more
exaly   +3 more sources

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
exaly   +2 more sources

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 Brandao dos Santos, Silvio Jamil Ferzoli Guimarães, Jefersson Alex 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, 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
openaire   +1 more source