ESTIMATION OF LANDCOVER TYPES OVER HIMALAYAN REGION WITH THE CLASSIFICATION OF OPTICAL AND MICROWAVE-BASED IMAGE FUSION DATASET

Abstract

Himalayas play a significant role in terms of climate influence, the origin of rivers, hydropower generation, tourism, and forest wealth. The monitoring of the rugged terrain Himalayas via remote sensing is one of the efficient solutions to meet future requirements. In remote sensing, the sensors can be categorized as optical and microwave. The optical-based sensor provides multispectral or hyperspectral information at a very fine spatial resolution but is limited to daytime images without any penetration through the clouds. Whereas, the microwave works more effectively due to day/night image acquisition and cloud penetration capabilities. Therefore, the image fusion of multi-sensors (optical and microwave) datasets is important to extract crucial information about the Earth surface, especially over the Himalayas. However, the main aim of the article is to retrieve the different landcover types using various classifiers i.e., Linear Spectral Mixing (LSM), Random Forest Classifier (RFC), and Support Vector Machine (SVM) on the fused dataset. The dataset has been acquired over a part of Indian Himalayan terrain i.e., Uttarakhand State, India using microwave-based ISRO's Scatterometer Satellite (SCATSAT-1) and optical-based NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The results show the effectiveness of the RFC classifier in the mapping of land surface features as compared to other classification algorithms i.e., LSM and SVM. This study not only highlights the potential of the RFC classifier in the extraction of information but also, shows the significance of fusion of optical and microwave datasets in the extraction of important Earth surface features.