The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 701–705, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-701-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 701–705, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-701-2015

  11 Dec 2015

11 Dec 2015

CLUSTERING OF MULTI-TEMPORAL FULLY POLARIMETRIC L-BAND SAR DATA FOR AGRICULTURAL LAND COVER MAPPING

H. Tamiminia1, S. Homayouni2, and A. Safari1 H. Tamiminia et al.
  • 1School of Surveying and Geospatial Engineering, Dept. of Remote Sensing, College of Engineering, University of Tehran, Iran
  • 2Dept. of Geography, Environmental Studies and Geomatics, University of Ottawa, Ottawa, Canada

Keywords: Kernel-Based Fuzzy C-means, Crop Classification, Polarimetric SAR Images, Multi-Temporal Data, Target Decompositions

Abstract. Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.