EVALUTION OF MACHINE LEARNING METHODS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

被引：3

作者：

Kumar, M. Suresh ^{[1
]}

Keerthi, V. ^{[1
]}

Anjnai, R. N. ^{[1
]}

Sarma, M. Manju ^{[1
]}

Bothale, Vinod ^{[1
]}

机构：

[1] ISRO, Hyderabad 500037, India

来源：

2020 IEEE INDIA GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (INGARSS) | 2020年

关键词：

Convolution Neural Network; Hyper spectral images; SVM; RF;

D O I：

10.1109/InGARSS48198.2020.9358916

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Machine learning algorithms are outstanding predictive powerful tools for classification of hypserspectral images. In this paper we summarize the various classification techniques based on machine learning approaches for space borne hypserspectral images. Random Forest (RF), Support Vector Machine (SVM) and a deep learning technique, Convolution Neural Network (CNN) are explored on HySIS images. CNN shows great potential to yield high performance in hypserspectral image classification. 2-D and 3-D CNN techniques provided robust classification results when compared to RF, SVM methods.

引用

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页码：225 / 228

页数：4

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