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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Lin, Y., Li, D., Zhao, C., Xu, J., Zhang, B. (2020). Haralick Feature Guided Network for the Improvement of Generalization in Landcover Classification. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_5
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DOI: https://doi.org/10.1007/978-3-030-37548-5_5
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