Results 211 to 220 of about 4,953 (252)
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Constrained band selection for hyperspectral imagery
IEEE Transactions on Geoscience and Remote Sensing, 2006Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (
Chein-I Chang, Su Wang 0002
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Optimal Clustering Framework for Hyperspectral Band Selection [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2018 Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal ...
Xuelong Li, Qi Wang, Xuelong Li
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Hyperspectral Band Selection via Rank Minimization
IEEE Geoscience and Remote Sensing Letters, 2017Band selection is an important preprocessing technique for hyperspectral imagery, through which a subset of critical and representative spectral bands can be selected from a raw image cube for compact yet effect representation. Among the valid selection strategies, performing band selection in an unsupervised manner is usually considered more general ...
Guokang Zhu, Ning Wei, Shuying Li
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Methodology for Hyperspectral Band Selection
Photogrammetric Engineering & Remote Sensing, 2004While hyperspectral data are very rich in information, processing the hyperspectral data poses several challenges regarding computational requirements, information redundancy removal, relevant information identification, and modeling accuracy.
Peter Bajcsy, Peter Groves
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Adaptive hyperspectral band selection
SPIE Proceedings, 2005We present a new technique for adaptive band selection from hyperspectral image cubes for detecting small targets using an anomaly detector. The proposed technique ensures the selection of lowest number of spectral bands using Mahalanobis distance, maximum affordable extra noise variance, and Constant False Alarm Rate (CFAR) anomaly detector threshold.
M. S. Alam, S. Ochilov
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Hyperspectral Band Selection Based on Endmember Dissimilarity for Hyperspectral Unmixing
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Hyperspectral remote sensing could acquire hundreds of bands to cover a complete spectral interval, which deliver more information and allow a whole range of new and more precise applications. But vast data volume can cause trouble in computer processing and data transmission.
Mingming Xu 0001 +6 more
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Gray Wolf Optimizer for hyperspectral band selection
Applied Soft Computing Journal, 2016Graphical abstractDisplay Omitted HighlightsWe propose a new approach for feature selection in hyperspectral image classification.The problem of band selection is reformulated as a combinatorial problem.We design a new objective function which takes into account two term, the classification error rate and the class separability distance.To optimize the
Abdelkader Benyettou
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Hyperspectral band selection based on evolutionary optimization
2013 Ninth International Conference on Natural Computation (ICNC), 2013A hyperspectral image consists of a series of spectral bands which has brought great challenges to image processing and analysis. To alleviate the curse of dimensionality, band selection is therefore applied to the hyperspectral images. In this paper, a two-step method is proposed for band selection.
Qiannan Du +3 more
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Nature-Inspired Framework for Hyperspectral Band Selection
IEEE Transactions on Geoscience and Remote Sensing, 2014Although hyperspectral images acquired by on-board satellites provide information from a wide range of wavelengths in the spectrum, the obtained information is usually highly correlated. This paper proposes a novel framework to reduce the computation cost for large amounts of data based on the efficiency of the optimum-path forest (OPF) classifier and ...
Rodrigo Y. M. Nakamura +5 more
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A Coarse-to-Fine Optimization for Hyperspectral Band Selection
IEEE Geoscience and Remote Sensing Letters, 2019Hyperspectral band selection is a feature selection method that selects a most representative set of bands to achieve a good performance in several tasks such as classification and anomaly detection. It reduces the burden of storage, transmission, and computation. In this letter, a two-stage band selection algorithm is introduced.
Xuefeng Jiang +4 more
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