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Constrained Band Subset Selection for Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2017This 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 +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), 2002We 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, 2007Band 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
2018Band 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 +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), 2014A 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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An Efficient Method for Supervised Hyperspectral Band Selection
IEEE Geoscience and Remote Sensing Letters, 2011Band selection is often applied to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance.
He Yang +3 more
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Optimal Neighboring Reconstruction for Hyperspectral Band Selection
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Band selection, as an effective and popular dimensional reduction methods for hyperspectral image (HSI), has raised wide attention in recent years. In this paper, we propose a novel band selection method called optimal neighboring reconstruction (ONR). Compared to conventional methods, ONR mainly has following advantages.
Fahong Zhang 0003 +2 more
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Constrained multiple band selection for hyperspectral imagery
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016A 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 +3 more
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Hyperspectral Band Selection Based on Rough Set
IEEE Transactions on Geoscience and Remote Sensing, 2015Band selection is a well-known approach to reduce the dimensionality of hyperspectral imagery. Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of the hyperspectral imagery ...
Patra, Swarnajyoti +2 more
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IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008
This paper concentrates on reducing the computational complexity of hyperspectral image band selection algorithms via one-bit transform which can be obtained using simple filtering and comparison operations. Firstly, one-bit transform of each band is obtained and noisy and less-discriminative bands, which are decided according to the total number of ...
Demir, Begum, Ertürk, Sarp
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This paper concentrates on reducing the computational complexity of hyperspectral image band selection algorithms via one-bit transform which can be obtained using simple filtering and comparison operations. Firstly, one-bit transform of each band is obtained and noisy and less-discriminative bands, which are decided according to the total number of ...
Demir, Begum, Ertürk, Sarp
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