Spatial and Spectral Analysis of Resampling Algorithms in Image Fusion of Optical and Microwave Satellite Images: A Case Study Over Western Himalayas

Abstract

The Western Himalayas receive significant snowfall, and it's essential to monitor the snow cover for various purposes like managing water resources, conducting hydrological research, and predicting avalanches using remote sensing techniques. With the pan-sharpening of different satellite sensor images, the essential features can be obtained to understand the snow cover dynamics. The resampling algorithms play a very important role in the pan-sharpening procedure to highlight the important features of the pan-sharpened dataset. However, the performance of various resampling algorithms for pan-sharpening is very rarely validated with optical and microwave datasets. In this article, the spatial and spectral analysis of different resampling algorithms, i.e., Nearest Neighbour (NN), Bilinear (BI), and Cubic Convolution (CC) resampling, have been performed using the fusion of optical and microwave satellite images. In this study, two datasets, i.e., MODIS (optical) and SCATSAT-1 (microwave) were used over the western Himalayas (i.e., Ladakh, Jammu and Kashmir, Himachal Pradesh, and Uttrakhand). To evaluate the performance of each resampled pan-sharpened dataset, the output is classified using a Support Vector Machine (SVM) classifier and a Spectral Angle Mapper (SAM) classifier. Root Mean Square Error values were computed to quantify the level of agreement between the pan-sharpened datasets and the ground truth data. The result of statistical analysis showed that NN-based pan-sharpened performed better than BI-based and CC-based pan-sharpened classified images with both classifiers i.e., SVM and SAM. This study is important in terms of the effective utilization of the resampling techniques along with pan-sharpening algorithms.