Response of topographic control on nearest-neighbor diffusion-based pan-sharpening using multispectral MODIS and AWiFS satellite dataset

Abstract

Remote sensing plays a significant role in monitoring of the undulating the Himalayas. With continuous monitoring, the preservation of natural resources and mitigation of natural hazards is possible. Currently, satellite sensors are not capable enough to deliver the earth surface image at a very high temporal, spectral, and spatial resolution, simultaneously. Therefore, it is essential to perform the pan-sharpening of spatially high-resolution (HR) panchromatic (PAN) spectral band with low-resolution (LR) multispectral (MS) imagery which must be acquired on the same temporal date from multiple sensors. On the other hand, due to the rugged topography of the Himalayas, topographic effects are generally induced in the form of shadow and affect the spatial information or spectral information. Therefore, the focus of the present work is to implement and analyze the performance of topographic correction on nearest-neighbor diffusion (NND)-based pan-sharpening algorithm. In order to evaluate the effectiveness, K-mean classification (KMC) has been implemented over topographically corrected and topographically uncorrected NND pan-sharpened images. From the experimental outcomes, it is inferred that topographically corrected NND pan-sharpened classified image has achieved better accuracy (81.33%) as compared with topographically uncorrected NND pan-sharpened classified imagery (78%). It is expected that the integration of topographic correction and NND algorithm facilitates the better extraction of spectral and spatial information and leads to improvement in results for further analysis such as change detection procedure and classification. The applications of the present study are in monitoring of climate change and mapping land cover changes over rugged terrain regions.