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2020 | OriginalPaper | Chapter

Haralick Feature Guided Network for the Improvement of Generalization in Landcover Classification

Authors : Yuzhun Lin, Daoji Li, Chuan Zhao, Junfeng Xu, Baoming Zhang

Published in: Pattern Recognition and Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

This study examined the application of semantic segmentation in landcover classification, a recently popular task in the field of remote sensing. Most semantic segmentation methods exhibit strong sample dependence. This tends to have high prediction accuracy in similar areas, but low accuracy in other areas or the same area at different time phases. Our approach utilizes three Haralick features to enhance the generalization ability. In addition, several variants were also implemented for comparison. We found that these features can effectively improve generalization of landcover classification.

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Title: Haralick Feature Guided Network for the Improvement of Generalization in Landcover Classification
Authors: Yuzhun Lin
Daoji Li
Chuan Zhao
Junfeng Xu
Baoming Zhang
Publisher: Springer International Publishing
Book: Pattern Recognition and Artificial Intelligence
Print ISBN: 978-3-030-37547-8

Electronic ISBN: 978-3-030-37548-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-37548-5_5

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