| IMPACT OF POLARIMETRIC SAR SPECKLE REDUCTION ON CLASSIFICATION OF AGRICULTURE LANDS |
| Paper ID : 1270-SMPR |
| Authors: |
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Ramin Farhadiani *1, Saeid Homayouni2, Abdolreza Safari1 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran 2Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec, Canada |
| Abstract: |
| Presence of speckle in the Polarimetric Synthetic Aperture Radar (PolSAR) images could decrease the performance of information extraction applications such as classification, segmentation, change detection, etc. Hence, an essential pre-processing step named de-speckling is needed to suppress this granular noise-like phenomenon from the PolSAR images. In this paper, a comparison study has been accomplished between several recent PolSAR speckle reduction methods such as POSSC, PNGF, and ANLM. For this comparison, a 4-look L-band AIRSAR NASA/JPL PolSAR dataset that obtained over an agriculture land from Flevoland, Netherlands, was employed. The de-speckling assessment was completed based on some non-referenced quantitative indicators. All de-speckling methods were evaluated in the case of speckle reduction form homogeneous areas, details, and radiometric preservation, and retain the polarimetric information. Furthermore, the impact of PolSAR de-speckling on classification was evaluated. For this purpose, Support Vector Machine (SVM) classifier was used to classify H/A/Alpha decompositions. Experimental results showed that the ANLM method was better to suppress the speckle, followed by the PNGF method. Also, the classification results showed that a proper PolSAR de-speckling could effectively increase the classification accuracy. The improvement of the overall accuracy based on de-speckling by the ANLM method was approximately 22% and 13% than the POSSC and PNGF methods, respectively. |
| Keywords: |
| Synthetic Aperture Radar, De-speckling, Classification, Support Vector Machine, Crop Map |
| Status : Conditional Accept (Oral Presentation) |
