Journal of Remote Sensing & GIS

Department of Petroleum Engineering, Texas A&M University, College Station, Texas, United States of America

Publications

• Research Article   
  Enhancing Hyperspectral Image Resolution with Deep Transfer Learning: A Case Study on Model Deployment and Evaluation
  Author(s): Nafis Khan, Siddharth Misra, James Omeke*, Priyanka Chukka and Siddhanth Sirgapoor
  
  Hyperspectral imaging holds immense potential for detailed land cover analysis due to its rich spectral information across the electromagnetic spectrum. However, the inherent trade-off between spatial and spectral resolution limits its applicability. The study explores the deployment and enhancement of Single Hyperspectral Image Super-Resolution (SSPSR) model, using the Spatial-Spectral Prior Network (SSPN), to notably improve the spatial and spectral quality of hyperspectral images. This model stands out for its ability to elevate image resolution without relying on supplementary hardware enhancements. Our research focused on training this model using the comprehensive Chikusei dataset, this dataset features 128 spectral bands from 363 nm to 1018 nm, captured over Chikusei, Japan, enhancing deep learning research in agricultural and urban land cover analysis, followed b.. View more»
  

  Abstract PDF