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Convolutional Neural Network (CNN) is achieving remarkable progress invarious computer vision tasks. In the past few years, the remote sensingcommunity has observed Deep Neural Network (DNN) finally taking off in severalchallenging fields. In this study, we propose a DNN to generate a predefinedHigh Resolution (HR) synthetic spectral band using an ensemble of concurrentLow Resolution (LR) bands and existing HR bands. Of particular interest, theproposed network, namely DeepSWIR, synthesizes Short-Wave InfraRed (SWIR) bandat 5m Ground Sampling Distance (GSD) using Green (G), Red (R) and Near InfraRed(NIR) bands at both 24m and 5m GSD, and SWIR band at 24m GSD. To our knowledge,the highest spatial resolution of commercially deliverable SWIR band is at 7.5mGSD. Also, we propose a Gaussian feathering based image stitching approach inlight of processing large satellite imagery. To experimentally validate thesynthesized HR SWIR band, we critically analyse the qualitative andquantitative results produced by DeepSWIR using state-of-the-art evaluationmetrics. Further, we convert the synthesized DN values to Top Of Atmosphere(TOA) reflectance and compare with the corresponding band of Sentinel-2B.Finally, we show one real world application of the synthesized band by using itto map wetland resources over our region of interest.

DeepSWIR: A Deep Learning Based Approach for the Synthesis of Short-Wave InfraRed Band using Multi-Sensor Concurrent Datasets