Image Fusion of SAR and Optical Images for Identifying Antarctic Ice Features

Remote sensing data plays an important role in extracting thematic information from various sensors having different spectral, spatial and temporal resolutions. The present study aims at fusion of Radar Imaging Satellite-1 Fine Resolution Stripmap-1 and ResourceSAT-2 Linear Imaging Self Scanning Scanner-4 (LISS-4) images over Indian Antarctic Research Station Maitri and its surroundings to generate a better product which contains the characteristics of both the spectral information from LISS-4 and the spatial details of SAR. Different pixel-based fusion techniques such as Brovey Transform, Principal Component Analysis (PCA), Intensity Hue Saturation and Wavelet Principal Component Analysis (W-PCA) have been used in the present study. These image fusion techniques have been applied for the whole scene as well as for individual surface features like melt ponds, crevasses, freshwater lake, blue ice, oasis and lake ice for better discrimination of features. Quality assessment is performed by evaluating the performance of these algorithms using visual, spatial (High Pass Correlation Coefficient and Entropy) and spectral (Root Mean Square Error, Correlation Coefficient, ERGAS and Universal Quality Index) parameters. It is found that the identification of certain features such as crevasses, blue ice, melt ponds and lake ice has been improved with fused images compared to the original multi-spectral and SAR images. PCA and W-PCA fusion techniques offer better performance as compared to the rest of the techniques.