Results 231 to 240 of about 5,679 (259)

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
openaire   +1 more source

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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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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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
openaire   +1 more source

Constrained Band Subset Selection for Hyperspectral Imagery

IEEE Geoscience and Remote Sensing Letters, 2017
This letter extends the constrained band selection (CBS) technique to constrained band subset selection (CBSS) in a similar manner that constrained energy minimization has been extended to linearly constrained minimum variance. CBSS constrains multiple bands as a band subset as opposed to CBS constraining a single band as a singleton set.
Lin Wang 0028, Hsiao-Chi Li, Bai Xue, Chein-I Chang   +3 more
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Best bands selection for detection in hyperspectral processing

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
We explore the role of best bands algorithms in the context of maximizing the performance of hyperspectral algorithms. Specifically, we first focus on creating an intuitive framework for how metrics quantify the distance between two spectra. Focusing on the spectral angle mapper (SAM) metric, we demonstrate how the separability of two spectra can be ...
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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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Hyperspectral Band Selection with Convolutional Neural Network

2018
Band selection is a kind of dimension reduction method, which tries to remove redundant bands and choose several pivotal bands to represent the entire hyperspectral image (HSI). Supervised band selection algorithms tend to perform well because of the introduction of prior information.
Rui Cai 0002, Yuan Yuan 0001, Xiaoqiang Lu   +2 more
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Hyperspectral band selection using firefly algorithm

2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014
A novel band selection algorithm for hyperspectral dimensionality reduction by improving the firefly algorithm is put forward. Specifically, the framework which using bio-inspired algorithm for hyperspectral band selection is described; the between-class separability criteria such as Jeffreys-Matusita (JM) distance, transformation divergence (TD) are ...
Hongjun Su, Qiannan Li, Peijun Du
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Constrained multiple band selection for hyperspectral imagery

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
A recent developed band selection, called constrained band selection (CBS), makes use of constrained energy minimization (CEM) to constrain a single band to calculate its priority for band selection (BS). This paper extends such CEM-BS to a constrained multiple band selection (CMBS)-based method, to be called linearly constrained minimum variance ...
Hsiao-Chi Li, Chein-I Chang, Lin Wang 0028, Yao Li 0008   +3 more
openaire   +1 more source