Results 211 to 220 of about 4,471 (236)

Methodology for Hyperspectral Band Selection

Photogrammetric Engineering & Remote Sensing, 2004
While 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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Hyperspectral Band Selection Based on Endmember Dissimilarity for Hyperspectral Unmixing

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Hyperspectral 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, Yuxiang Zhang 0001, Jie Li 0022, Jiayi Li, Dongmei Song, Yanguo Fan, Ning Sun   +6 more
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Hyperspectral band selection based on evolutionary optimization

2013 Ninth International Conference on Natural Computation (ICNC), 2013
A 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, Aimin Zhou, Cong Liu 0011, Guixu Zhang   +3 more
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A Coarse-to-Fine Optimization for Hyperspectral Band Selection

IEEE Geoscience and Remote Sensing Letters, 2019
Hyperspectral 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, Jianzhe Lin, Junrui Liu, Shuying Li, Yanning Zhang 0001   +4 more
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Hyperspectral Band Selection via Rank Minimization

IEEE Geoscience and Remote Sensing Letters, 2017
Band 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, Yuancheng Huang, Shuying Li, Jun Tang 0007, Dong Liang 0009   +4 more
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Hyperspectral band selection based on graph clustering

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012
In this paper we present a new method for hyperspectral band selection problem. The principle is to create a band adjacency graph (BAG) where the nodes represent the bands and the edges represent the similarity weights between the bands. The Markov Clustering Process (abbreviated MCL process) defines a sequence of stochastic matrices by alternation of ...
Rachid Hedjam, Mohamed Cheriet
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Hyperspectral Band Selection by Multitask Sparsity Pursuit

IEEE Transactions on Geoscience and Remote Sensing, 2015
Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the ...
Yuan Yuan 0001, Guokang Zhu, Qi Wang 0009   +2 more
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Gray Wolf Optimizer for hyperspectral band selection

Applied Soft Computing, 2016
Graphical 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
Seyyid Ahmed Medjahed, Tamazouzt Ait Saadi, Abdelkader Benyettou, Mohammed Ouali   +3 more
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Nature-Inspired Framework for Hyperspectral Band Selection

IEEE Transactions on Geoscience and Remote Sensing, 2014
Although 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, Leila Maria Garcia Fonseca, Jefersson Alex dos Santos, Ricardo da Silva Torres, Xin-She Yang 0001, João P. Papa   +5 more
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Representative band selection for hyperspectral image classification

Journal of Visual Communication and Image Representation, 2017
Abstract High dimensional curse for hyperspectral images is one major challenge in image classification. In this work, we introduce a novel spectral band selection method by representative band mining. In the proposed method, the distance between two spectral bands is measured by using disjoint information.
Ronglu Yang, Lifan Su, Xibin Zhao, Hai Wan, Jiaguang Sun 0001   +4 more
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