Semi-supervised Classification of Hyperspectral Images with Small Sample Sizes

被引:0
|
作者
Aydemir, M. Said [1 ,2 ]
Bilgin, Gokhan [2 ]
机构
[1] TUBITAK, BILGEM, TR-41470 Kocaeli, Turkey
[2] Yildiz Tekn Univ, Bilgisayar Muhendisligi Bolumu, TR-34220 Istanbul, Turkey
关键词
Hyperspectral images; semi-supervised learning; deep neural networks; convolutional neural networks; deep learning; REGRESSION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the classification of hyperspectral images with supervised methods, acquisition of ground-truth information for a hyperspectral image is a challenging process in terms of time and cost. Besides, amount of the labeled data also affects the performance of classifiers. In this study, as a solution to this problem, a hyperspectral image classifier is proposed with semisupervised learning, support vector machine classifier and deep learning. In the first phase to improve the classification performance, limited number of training data is increased by semisupervised learning methodology. Then, the classification process is performed with support vector machines and convolutional neural networks. According to the acquired classification results, a close classification performance is obtained by the system with small number of training data to the supervised classification. Furthermore, deep neural network has reached more successful results than support vector machines.
引用
收藏
页码:681 / 684
页数:4
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