Matrix decomposition based salient object detection in hyperspectral imagery

Yifan Gao, Hangqi Yan, Lei Zhang, Runping Xi, Yanning Zhang, Wei Wei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Salient detection in hyperspectral images (HSIs) can be benefited by the abundant spectral information. Most related methods adopt integrating the spectral characteristics into the traditional Itti's model to consider the local region contrast. However, these methods often segmente the object into several pieces and are sensitive to uneven illumination. To address these problems, we propose a novel matrix decomposition based salient object detection method for HSIs. With being modelled with spectral gradient feature, the HSI is decomposed into a low-rank background matrix with a sparse one which can indicate the salient object with more intact appearance. In addition, the spectral gradient feature guarantees the proposed method to perform robustly with uneven illumination. Experimental results demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
EditorsLiang Zhao, Lipo Wang, Guoyong Cai, Kenli Li, Yong Liu, Guoqing Xiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages574-577
Number of pages4
ISBN (Electronic)9781538621653
DOIs
StatePublished - 21 Jun 2018
Event13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 - Guilin, Guangxi, China
Duration: 29 Jul 201731 Jul 2017

Publication series

NameICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

Conference

Conference13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Country/TerritoryChina
CityGuilin, Guangxi
Period29/07/1731/07/17

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