Share Email Print
cover

Proceedings Paper • new

Hyperspectral image classification with imbalanced data based on oversampling and convolutional neural network
Author(s): Lei Cai; Geng Zhang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Data imbalance is a common problem in hyperspectral image classification. The imbalanced hyperspectral data will seriously affect the final classification performance. To address this problem, this paper proposes a novel solution based on oversampling method and convolutional neural network. The solution is implemented in two steps. Firstly, SMOTE(Synthetic Minority Oversampling Technique) is used to enhance the data of minority classes. In the minority classes, SMOTE method is used to generate new artificial samples, and then the new artificial samples are added to the minority classes, so that all classes in the training dataset can reach to the balanced distribution. Secondly, According to the data characteristics of hyperspectral image, a convolutional neural network is constructed for classifying the hyperspectral image. The balanced training data set is used to train the convolutional neural network. We experimented with the proposed solution on the Indian Pines, Pavia University dataset. The experimental results show that the proposed solution can effectively solve the problem of imbalanced hyperspectral data and improve the classification performance.

Paper Details

Date Published: 18 December 2019
PDF: 7 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420B (18 December 2019); doi: 10.1117/12.2543458
Show Author Affiliations
Lei Cai, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Sciences (China)
Geng Zhang, Xi'an Institute of Optics and Precision Mechanics (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

© SPIE. Terms of Use
Back to Top