research-article

An Unsupervised Band Selection Based on Band Similarity for Hyperspectral Image Target Detection

Authors:

Yan Cao,

Jing Zhang,

Li Zhuo,

Chao Wang,

Qianlan ZhouAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 336 - 339

https://doi.org/10.1145/2632856.2632865

Published: 10 July 2014 Publication History

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Abstract

In remote sensing data processing, band selection is very important for hyperspectral image processing and analysis, which utilize the most distinctive and informative band subset of original bands to reduce data dimensionality. Although band selection can significantly alleviate the computational burden, the process itself may cause additional computation complexity. In this paper, an unsupervised band selection method based on band similarity is proposed for hyperspectral image target detection. Several selected pixels are used for unsupervised band selection instead of using all the pixels to reduce computational complexity. The number of bands to be selected is determined by adjusting the threshold of similarity metric, to ensure target detection operator have the best performance with selected bands. The experimental results show that our method can yield a better result in target detection.

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Cited By

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Patro RSubudhi SBiswal PDell'acqua F(2021)A Review of Unsupervised Band Selection Techniques: Land Cover Classification for Hyperspectral Earth Observation DataIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2021.30519799:3(72-111)Online publication date: Sep-2021
https://doi.org/10.1109/MGRS.2021.3051979
Zhao MYue LHu JDu SLi PWang L(2021)Salient target detection in hyperspectral image based on visual attentionIET Image Processing10.1049/ipr2.1219715:10(2301-2308)Online publication date: 2-Apr-2021
https://doi.org/10.1049/ipr2.12197
Sharifi Hashjin SKhazai S(2020)A new method to detect targets in hyperspectral images based on principal component analysisGeocarto International10.1080/10106049.2020.183162537:9(2679-2697)Online publication date: 1-Dec-2020
https://doi.org/10.1080/10106049.2020.1831625
Show More Cited By

Index Terms

An Unsupervised Band Selection Based on Band Similarity for Hyperspectral Image Target Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Information & Contributors

Information

Published In

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

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Patro RSubudhi SBiswal PDell'acqua F(2021)A Review of Unsupervised Band Selection Techniques: Land Cover Classification for Hyperspectral Earth Observation DataIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2021.30519799:3(72-111)Online publication date: Sep-2021
https://doi.org/10.1109/MGRS.2021.3051979
Zhao MYue LHu JDu SLi PWang L(2021)Salient target detection in hyperspectral image based on visual attentionIET Image Processing10.1049/ipr2.1219715:10(2301-2308)Online publication date: 2-Apr-2021
https://doi.org/10.1049/ipr2.12197
Sharifi Hashjin SKhazai S(2020)A new method to detect targets in hyperspectral images based on principal component analysisGeocarto International10.1080/10106049.2020.183162537:9(2679-2697)Online publication date: 1-Dec-2020
https://doi.org/10.1080/10106049.2020.1831625
Bradley PKeller SWeinmann M(2018)Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral DataRemote Sensing10.3390/rs1010156410:10(1564)Online publication date: 29-Sep-2018
https://doi.org/10.3390/rs10101564
Cao YZhang JTian QZhuo LZhou Q(2015)Salient target detection in hyperspectral images using spectral saliency2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)10.1109/ChinaSIP.2015.7230572(1086-1090)Online publication date: Jul-2015
https://doi.org/10.1109/ChinaSIP.2015.7230572

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Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery