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In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agriculturaland geological applications. While these spectral signatures of the objects of interest provide important clues, the relativelypoor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancementusing multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its usefor classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-offbetween improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy ofusing vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensorsimulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approachdemonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuraciesover mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metricshows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures

Sensor Simulation based Hyperspectral Image Enhancement with Minimal Spectral Distortion