Hyperspectral image classification with SVM and guided filter View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Yanhui Guo, Xijie Yin, Xuechen Zhao, Dongxin Yang, Yu Bai

ABSTRACT

Hyperspectral image (HSI) classification has been long envisioned in the remote sensing community. Many methods have been proposed for HSI classification. Among them, the method of fusing spatial features has been widely used and achieved good performance. Aiming at the problem of spatial feature extraction in spectral-spatial HSI classification, we proposed a guided filter-based method. We attempted two fusion methods for spectral and spatial features. In order to optimize the classification results, we also adopted a guided filter to obtain better results. We apply the support vector machine (SVM) to classify the HSI. Experiments show that our proposed methods can obtain very competitive results than compared methods on all the three popular datasets. More importantly, our methods are fast and easy to implement. More... »

PAGES

56

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13638-019-1346-z

DOI

http://dx.doi.org/10.1186/s13638-019-1346-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112646031


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