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  • On the sampling strategies for evaluation of joint spectral-spatial information based classifiers

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    Accepted Manuscript (AM)
    Author(s)
    Zhou, Jun
    Liang, Jie
    Qian, Yuntao
    Gao, Yongsheng
    Tong, Lei
    Griffith University Author(s)
    Gao, Yongsheng
    Zhou, Jun
    Year published
    2015
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    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 ...
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    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.
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    Conference Title
    2015 7TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS)
    Volume
    2015-June
    DOI
    https://doi.org/10.1109/WHISPERS.2015.8075474
    Copyright Statement
    © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Image processing
    Publication URI
    http://hdl.handle.net/10072/355891
    Collection
    • Conference outputs

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