Super-resolution mapping of hyperspectral satellite images using hybrid genetic algorithm

To assess the rate of sedimentation and the consequent reduction in the storage capacity, periodical capacity surveys of multi-purpose reservoirs is essential. Hydrographic surveys and acoustic surveys are time-consuming and expensive. The limited availability and high cost of the high-resolution images require a different methodology to accurately estimate the water-spread area of the reservoir. In this study, 30 m resolution hyperspectral image (hyperion) and multi-spectral image (The Earth Observing One (EO-1) advanced land imager) are used to estimate the water-spread area of the Peechi Reservoir, South India. A hybrid genetic algorithm (GA)-based super-resolution mapping approach is developed and demonstrated, which incorporates the multi-objective GA and Hopfield neural network (HNN). The hybrid GA-based super-resolution mapping approach gives a global optimum solution in half of the original computation time. Furthermore, mapping approach gives an error of 6.38% for the multi-spectral image and a lesser error of 3.86% for the hyperspectral image, while the HNN-based super-resolution mapping approach gives an error of 8.23% for the multi-spectral image and 5.71% for the hyperspectral image. Thus, in this work, an efficient technique based on hybrid GA is presented, which is a useful tool for accurate mapping of water bodies at the sub-pixel scale using hyperspectral imagery.