The operational capability of optical sensors are limited under cloud cover conditions and alternatively the all weather capability of sensors operating in microwave region play an important role in a tropical country like India where the persistence of cloud cover is high. This paper deals with the evaluation of ERS-1 Synthetic Aperture Radar (SAR) data in conjunction with Indian Remote Sensing Data (IRS) LISS-II optical data in synergistic combinations with microwave data for land use/land cover mapping and assessment. Here part of East Godavari District of Andhra Pradesh, India covering coringa reserved forest and Kakinada town and its surroundings covering 1900 sq.kms of area has been considered for the study. The area selected encompasses a variety of land use/land cover features like urban areas, croplands, plantations, mangroves, marshy and waterlogged areas, salt pans, waterbodies and aquaculture sites. The data from coherent imaging systems like SAR are corrupted with speckle noise and in order to minimise speckle different filtering techniques have been applied on SAR raw data. It has been observed that the 5x5 median filter could adequately minimise the speckle interference in feature delineation. Land use/land cover maps have been generated from raw and filtered SAR data sets and also the mapping accuracy has been estimated using Kappa and proportional accuracy statistical methods. The increased mapping accuracy in filtered SAR data has been attributed essentially to reduction of speckle noise. False color composites (FCC) of different combinations of filtered and registered data products of LISS-II and SAR data have been generated. Intensity-Hue Saturation (IHS) transformation and Principal Component Analysis have been applied to the registered data sets. Ranking table quantifying advantages of different combinations has beenprepared. From the analysis, it has been concluded that synergistic combination of optical and microwave data helps in delineating land use/land cover categories upto Level-III compared to individual data sets.
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