Classification of Polarimetric SAR Data Based on Object-Based Multiple Classifiers for Urban Land-Cover
详细信息 查看全文
- 作者：Masoud Habibi ; Mahmod Reza Sahebi…
- 关键词：PolSAR ; Multiple classifier ; Object ; based classification ; SVM
- 刊名：Journal of the Indian Society of Remote Sensing
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：44
- 期：6
- 页码：855-863
- 全文大小：1,384 KB
- 刊物类别：Earth and Environmental Science
- 刊物主题：Earth sciences
Geosciences
Remote Sensing and Photogrammetry - 出版者：Springer India
- ISSN：0974-3006
- 卷排序：44
文摘
In this study, we used an object-oriented method for merging pixel-based classification and image segments to get an optimal classification result for an urban land-cover classification which is one of the important applications of Polarimetric SAR (PolSAR) remote-sensing. Because of the nature of PolSAR images, various features can be extracted and used in a classification. To achieve an improved classification accuracy, an optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with a support vector machine (SVM) as a pixel-based classification with the class accuracy as the criterion in feature selection. We proposed the SVM margin as a new distance criterion for feature selection. In addition, to overcome the degradation of pixel-based classification results due to presence of speckle noise in PolSAR images, thematic features were used in image segmentation. In general, the proposed method consisted of three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result was merged with a set of segments that were obtained from multi-resolution segmentation and the overall accuracy of the results was evaluated. The objectives of the study were to improve the accuracy of classification and to introduce margin as the new criterion for feature selection. Our results showed that the overall accuracy of the proposed method (90.07 %) was improved compared with the single SVM classifier (80.46 %) and pixel-based multiple classifier (83.61 %). Lastly, the SVM margin distance as well as class accuracy could be considered as appropriate criteria in feature selection.