Comparative Evaluation of the Sensitivity of Multi-Polarised SAR and Optical Data for Various Land Cover Classes

Sugandh Chauhan, Hari Shanker Srivastava (Corresponding Author)

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The availability of reliable and timely land cover information is the basis to have sound economic planning and resource management for a modern nation like India. In this study, the capabilities of the dual polarimetric Envisat-1 ASAR and Landsat ETM+ data have been investigated for the land cover mapping. A comprehensive evaluation of the sensitivity of the cross-polarized (VH)/like-polarized (VV) ENVISAT-1 ASAR and optical data for various land cover classes has been done and a class separability analysis has been performed under different band combinations. In order to ensure maximum information retrieval, the bands MPDI (Microwave Polarization Difference Index) and NDVI (Normalized Difference Vegetation Index) have also been incorporated in the study. The separability among the class pairs have been analyzed using the Transformed Divergence (TD) procedure while the classification has been carried out using the Maximum Likelihood supervised classifier. The results of sensitivity analysis indicated that the vegetation is highly sensitive to the VH band owing to volume scattering while the built-up class could be more accurately distinguished in the VV band due to the corner reflector effect. The separability analysis further revealed that with the fusion of optical-VH polarised SAR data and the introduction of MPDI band to the multi-polarised SAR data, the separability among various class pairs greatly improved. The Landsat ETM+ and VH backscatter data fused image thus provided the highest classification accuracy of 91.25% with the kappa coefficient of 0.90, thus demonstrating its potential in land cover assessment and monitoring.
Original languageEnglish
Pages (from-to)01-14
JournalInternational Journal of Advancement in Remote Sensing, GIS and Geography
Issue number1
Publication statusPublished - Jan 2016
Externally publishedYes


  • Land cover mapping
  • Multi-polarised SAR data
  • Microwave Polarization Difference Index (MPDI)
  • Separability analysis
  • Multi-sensor satellite data


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