閱讀全文 | |
篇名 |
Hyperspectral Image Recognition Using SVM Combined Deep Learning
|
---|---|
並列篇名 | Hyperspectral Image Recognition Using SVM Combined Deep Learning |
作者 | Yifan Li、Junbao Li、Jeng-Shyang Pan |
英文摘要 | In this paper we present the conbination of deep learning and Support Vector Machine applied on the recognition of hyperspectal images. Hyperspectral image recognition is an essential problem in the practical hyperspectral imagery system. While deep learning is capable of reproducing feature vectors with great dimensions out of original data, it leads to great time cost and the Hugh phoenomenon. Such nonlinear problem is regarded as obstacles and kernel method appears to be a promising way to solve it. The performance of kernelbased learning system is influenced by the choices of kernel function and parameter greatly. We present the kernel learning method termed Support Vector Machine (SVM) applied on feature vectors supplied by deep learning upon hyperspectral image. The learning system is improved by adjusting the parameters and kernel functions to the data structure for improving performance on solving complex tasks. Experimental results validate the feasibility of the proposed methods. |
起訖頁 | 851-860 |
關鍵詞 | Deep learning、SVM、Hyperspectral image |
刊名 | 網際網路技術學刊 |
期數 | 201905 (20:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
|
QR Code | |
該期刊 上一篇
| A Study of Using Syntactic Cues in Short-text Similarity Measure |
該期刊 下一篇
| Cloud Storage: A Review on Secure Deduplication and Issues |