Unsupervised land-cover classification of interferometric SAR images

被引：1

作者：

Dammert, PBG ^{[1
]}

Kuhlmann, S ^{[1
]}

Askne, J ^{[1
]}

机构：

[1] Chalmers Univ Technol, Dept Radio & Space Sci, S-41296 Gothenburg, Sweden

来源：

IGARSS '98 - 1998 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, PROCEEDINGS VOLS 1-5: SENSING AND MANAGING THE ENVIRONMENT | 1998年

关键词：

D O I：

10.1109/IGARSS.1998.703658

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The current study evaluates an unsupervised segmentation method called fuzzy C-means for two multi-temporal interferometric SAR image datasets. Noise removal filters and a principal components transformation was carried out as a pre-processing step to reduce noise and input data amount. The final segmented images were optimally clustered into 2-3 classes/clusters where the classes are water-bodies, forested and non-forested areas. The actual classification accuracy has to be better validated with more up-to-date maps, but it seems to very promising for the two datasets respectively. The presented method is believed to be good for detection of forested areas, water-bodies and non-forested areas.

引用

页码：1805 / 1808

页数：4