The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method

• Published in: Canadian Journal of Remote Sensing
• Main Authors: Masoud Mahdianpari, Bahram Salehi, Fariba Mohammadimanesh
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: Taylor & Francis Group 2017
• Subjects: Environmental sciences; GE1-350; Technology; T; Canada; Newfoundland
• Online Access: https://doi.org/10.1080/07038992.2017.1381549; https://doaj.org/article/4074994b3c264517bd692eb488b13497

Speckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy.