Multi-scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF-SVM
文献类型：EI期刊论文
| 作者 | Luo J. ; Ming D. ; Shen Z. ; Wang M. ; Sheng H. |
| 发表日期 | 2007 |
| 关键词 | Automatic target recognition Image classification Image segmentation Remote sensing Support vector machines |
| 英文摘要 | This paper proposes the work flow of multi-scale information extraction from high resolution remote sensing images based on features: rough classification - parcel unit extraction (subtle segmentation) - expression of features - intelligent illation - information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)-Support Vector Machine (SVM), that is the image classification based on GMRF-SVM. This method integrates the advantages of GMRF-based texture classification and SVM-based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF-based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition. |
| 出处 | International Journal of Remote Sensing
 |
| 卷 | 28期:15页:3395-3412 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24712]  |
| 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 | Luo J.,Ming D.,Shen Z.,et al. Multi-scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF-SVM. 2007. |
入库方式： OAI收割
来源：地理科学与资源研究所
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