Urban area mapping from polarimetric SAR data using fuzzy inference system

Asmeet Ahluwalia; Surendar Manickam; Avik Bhattacharya; Alok Porwal

doi:10.1117/12.2223823

5 May 2016 Urban area mapping from polarimetric SAR data using fuzzy inference system

Asmeet Ahluwalia, Surendar Manickam, Avik Bhattacharya, Alok Porwal

Proceedings Volume 9877, Land Surface and Cryosphere Remote Sensing III; 987721 (2016) https://doi.org/10.1117/12.2223823
Event: SPIE Asia-Pacific Remote Sensing, 2016, New Delhi, India

Abstract

In this work, we present urban area mapping from full-polarimetric synthetic aperture radar (SAR) data using fuzzy inference system (FIS). In particular, our aim is to utilize the profound knowledge available about scattering mechanism from urban targets to delineate urban environment. In this approach, we have utilized the recently developed polarimetric SAR scattering power decomposition technique (SD-Y4O) given in Bhattacharya et. al. The improved powers along with some other polarimetric parameters were used in this study. A suitable normalization procedure was adapted to handle the skewness in the estimated parameters. The fuzzy if-then rules were constructed from the in-depth knowledge of scattering mechanisms from an urban environment. Suitable methods were introduced to deﬁne the fuzzy inference system. The defuzziﬁed membership values were thresholded using an unsupervised clustering method (k-means). The pixels lying in the range [μmax−σ, μmax+σ] corresponds to urban areas where µmax is the largest cluster center and σ is the standard deviation of the cluster corresponding to µmax. The extracted urban area is in visually good agreement with the high resolution optical image. ALOS PALSAR full-polarimetric L-band SAR data has been used in this study.

Citation Download Citation

Asmeet Ahluwalia, Surendar Manickam, Avik Bhattacharya, and Alok Porwal "Urban area mapping from polarimetric SAR data using fuzzy inference system", Proc. SPIE 9877, Land Surface and Cryosphere Remote Sensing III, 987721 (5 May 2016); https://doi.org/10.1117/12.2223823

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