Effect of Pansharpening in Fusion Based Change Detection of Snow Cover Using Convolutional Neural Networks

Abstract

Seasonal dynamics of snow cover is an essential area of research for hydrological modelling and water resource management. With the increased availability of remote sensing data, the timely information of the spatiotemporal distribution of snow cover is feasible at regular intervals. The primary objective of this study is to assess the effect of pansharpening in the accuracy of snow cover change detection in mountainous regions using freely available Landsat-8 multispectral data. In mountainous regions at the medium resolution, the changes at the mountain ridges are seldom identified. The incorporation of pansharpening in the change detection framework facilitates an improvement in the snow cover change detection at the ridges. For pansharpening, the PanNet architecture based on convolutional neural networks was adopted. A study area around Dhundi in the state of Himachal Pradesh in India was selected for the analysis. The experiments were carried out using a subset of Landsat-8 multispectral data acquired in the autumn and the winter seasons of 2017 and 2018, respectively. An improvement of 0.184 and 0.267 in the kappa coefficient was observed for the overall changes in the snow cover and at the ridges, respectively, based on the results from the proposed approach.