@inproceedings{285f52bc424a448b8a02deffaab2f6bf,
title = "Look for Saliency in Hyperspectral Images",
abstract = "Image saliency detection plays an important role in many vision tasks such as object detection, visualization, and classification. Many existing methods are applicable to trichromatic or grayscale images, but most of them are failure to remote sensing hyperspectral images with hundreds of bands. In this paper, a novel purity-aware saliency method is specifically proposed for hyperspectral images. Firstly, an autoencoder model for unmixing is designed to extract the abundance. Then, an abundance fraction vector norm is calculated as pixel spectral purity. On real-world hyperspectral images, our saliency detection method achieves good performance in holding structure information and purity distribution.",
keywords = "abundance, autoencoder, hyperspectral image, saliency",
author = "Zhiqi Shen and Xiaoyan Luo and Rui Xue and Hongyan Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
month = jul,
doi = "10.1109/IGARSS.2019.8898048",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2205--2208",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "美国",
}