@inproceedings{03227bd732c44d63910955ea7aeb7f5c,
title = "On the sampling strategies for evaluation of joint spectral-spatial information based classifiers",
abstract = "Joint spectral-spatial information based classification is an active topic in hyperspectral remote sensing. Current classification approaches adopt a random sampling strategy to evaluate the performance of various classification systems. Due to the limitation of benchmark data, sampling of training and testing data is performed on the same image. In this paper, we point out that while training with random sampling is practical for hyperspectral image classification, it has intrinsic problems in evaluating spectral-spatial information based classifiers. This statement is supported by several experiments, and has lead to the proposal of a new sampling strategy for comparing spectral spatial information based classifiers.",
keywords = "Hyperspectral classification, feature extraction, sampling, spectral-spatial analysis",
author = "Jun Zhou and Jie Liang and Yuntao Qian and Yongsheng Gao and Lei Tong",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 ; Conference date: 02-06-2015 Through 05-06-2015",
year = "2015",
month = jul,
day = "2",
doi = "10.1109/WHISPERS.2015.8075474",
language = "English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2015 7th Workshop on Hyperspectral Image and Signal Processing",
address = "United States",
}