@inproceedings{2470cf5bee61445cafb3063723116850,
title = "Matrix decomposition based salient object detection in hyperspectral imagery",
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.",
author = "Yifan Gao and Hangqi Yan and Lei Zhang and Runping Xi and Yanning Zhang and Wei Wei",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 ; Conference date: 29-07-2017 Through 31-07-2017",
year = "2018",
month = jun,
day = "21",
doi = "10.1109/FSKD.2017.8393333",
language = "英语",
series = "ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "574--577",
editor = "Liang Zhao and Lipo Wang and Guoyong Cai and Kenli Li and Yong Liu and Guoqing Xiao",
booktitle = "ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",
}