An efficient semi-supervised classification approach for hyperspectral imagery
文献类型：SCI/SSCI论文
| 作者 | Tan K. ; Li E. Z. ; Du Q. ; Du P. J. |
| 发表日期 | 2014 |
| 关键词 | Hyperspectral Semi-supervised learning Classification Segmentation Spectral-spatial feature SVM support vector machines remote-sensing images svm subspace space |
| 英文摘要 | In this paper, an efficient semi-supervised support vector machine (SVM) with segmentation-based ensemble ((SSVMSE)-S-2) algorithm is proposed for hyperspectral image classification. The algorithm utilizes spatial information extracted by a segmentation algorithm for unlabeled sample selection. The unlabeled samples that are the most similar to the labeled ones are found and the candidate set of unlabeled samples to be chosen is enlarged to the corresponding image segments. To ensure the finally selected unlabeled samples be spatially widely distributed and less correlated, random selection is conducted with the flexibility of the number of unlabeled samples actually participating in semi-supervised learning. Classification is also refined through a spectral-spatial feature ensemble technique. The proposed method with very limited labeled training samples is evaluated via experiments with two real hyperspectral images, where it outperforms the fully supervised SVM and the semi-supervised version without spectral-spatial ensemble. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. |
| 出处 | Isprs Journal of Photogrammetry and Remote Sensing |
| 卷 | 97 |
| 页 | 36-45 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0924-2716 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/29596]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Tan K.,Li E. Z.,Du Q.,et al. An efficient semi-supervised classification approach for hyperspectral imagery. 2014. |
入库方式： OAI收割
来源：地理科学与资源研究所
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