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Hyperspectral Band Selection: A Review
IEEE Geoscience and Remote Sensing Magazine, 2019A hyperspectral imaging sensor collects detailed spectral responses from ground objects using hundreds of narrow bands; this technology is used in many real-world applications. Band selection aims to select a small subset of hyperspectral bands to remove spectral redundancy and reduce computational costs while preserving the significant spectral ...
Qian Du, Weiwei Sun
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IEEE Transactions on Geoscience and Remote Sensing, 1999
Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality. Many criteria have been suggested in the past for optimal band selection. In this paper, a joint band-prioritization and band-decorrelation approach to band selection is considered for hyperspectral image classification.
Qian Du, Chein-I Chang
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Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality. Many criteria have been suggested in the past for optimal band selection. In this paper, a joint band-prioritization and band-decorrelation approach to band selection is considered for hyperspectral image classification.
Qian Du, Chein-I Chang
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Performance evaluation of band AMC using dynamic band selection [PDF]
2014 International Symposium on Wireless Personal Multimedia Communications (WPMC), 2014 Adaptive Modulation and Coding (AMC) is one of key technologies in 4G system. Band AMC technique enables the Orthogonal Frequency-Division Multiple Access (OFDMA) system to achieve their maximum throughput by efficient utilization of wireless channel characteristics.
Haesik Kim, Kim, Haesik
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Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2016 Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem.
Yuan Yuan +2 more
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A tri-band Frequency-Selective Surface
Journal of Electromagnetic Waves and Applications, 2021A novel low-profile tri-band bandpass frequency-selective surface (FSS) is proposed in this paper.
Mahaveer, Uttamchand +5 more
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Uniform Band Interval Divided Band Selection
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019This paper presents a new band selection approach, called uniform band interval divided band selection (UBIDBS) which uniformly divides a band range into a finite number of band intervals from which a band can be selected from each band interval according to a custom designed band prioritization (BP) criterion. Two BP criteria are introduced.
Fang Li +5 more
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Hyperspectral band selection for human detection
2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012Human detection based on spectral information is required for various applications, e.g. surveillance, tracking and missing person investigation. In practice, spectral human detection encounters the inherent challenge, i.e. multiple targets detection based on a limited number of spectral bands, because (1) there is a great variety in spectral profiles ...
Kuniaki Uto +3 more
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CLODD based band group selection
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of ...
Muhammad Aminul Islam +3 more
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Morphological Band Selection for Hyperspectral Imagery
IEEE Geoscience and Remote Sensing Letters, 2018In this letter, a novel morphological band selection method is proposed to obtain the most representative bands from hyperspectral image (HSI) in an unsupervised manner. In order to sufficiently process the HSI, we propose to use only a small set of data instead of using the original full data.
Wang, Jingyu +4 more
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2013
We have discussed a method of band selection in Chap. 4 where a specific subset of hyperspectral bands was selected from the input images based on the conditional entropy measure. We have also observed that one can achieve almost similar fusion output by using a small fraction of hyperspectral data.
Subhasis Chaudhuri, Ketan Kotwal
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We have discussed a method of band selection in Chap. 4 where a specific subset of hyperspectral bands was selected from the input images based on the conditional entropy measure. We have also observed that one can achieve almost similar fusion output by using a small fraction of hyperspectral data.
Subhasis Chaudhuri, Ketan Kotwal
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