@inproceedings{2ff73ecf6805477caccb8245ae9daaf9,
title = "Improving hyperspectral image classification using smoothing filter via sparse gradient minimization",
abstract = "In hyperspectral imagery, there exist homogeneous regions where neighboring pixels tend to belong to the same class with high probability. However, even though neighboring pixels are from the same material, their spectral characteristics may be different due to various factors, such as internal instrument noise or atmospheric scattering, which results in misclassification. In this work, the proposed framework employs a smoothing filter based on sparse gradient minimization, which is expected to eliminate the inherent variations within a small neighborhood. Experimental results for two hyperspectral image datasets demonstrate that the proposed algorithm significantly improve classification accuracy.",
keywords = "Image smoothing, hyperspectral data, pattern classification, sparse minimization",
author = "Wei Li and Wei Hu and Qiong Ran and Fan Zhang and Qian Du and Nicolas Younan",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2014 ; Conference date: 24-08-2014 Through 24-08-2014",
year = "2014",
month = sep,
day = "30",
doi = "10.1109/PRRS.2014.6914279",
language = "English",
series = "2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Du, {Jenny Qian} and Eckart Michaelsen and Bing Zhang",
booktitle = "2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2014",
address = "United States",
}