Super‐resolution mapping of hyperspectral satellite images using hybrid genetic algorithm. Issue 7 (5th April 2020)

• Record Type: Journal Article
• Authors: Cyril Amala Dhason, Heltin Genitha; Muthaia, Indhumathi; Sakthivel, Shanmuga Priyaa; Shanmugam, Sanjeevi
• Is Part Of: IET image processing. Volume 14:Issue 7(2020)
• Page Start: 1281
• Page End: 1290
• Publication Date: 2020-04-05
• Subjects: geophysical image processing -- genetic algorithms -- geophysical techniques -- hyperspectral imaging -- hydrological techniques -- Hopfield neural nets -- terrain mapping -- image resolution -- oceanographic techniques -- reservoirs -- remote sensing; hyperspectral satellite images -- multipurpose reservoirs -- hydrographic surveys -- acoustic surveys -- high‐resolution images -- water‐spread area -- EO‐1 advanced land imager -- Peechi Reservoir -- hybrid genetic algorithm‐based super‐resolution mapping approach -- hybrid GA‐based super‐resolution mapping approach -- HNN‐based super‐resolution mapping approach -- resolution hyperspectral image -- Hopfield neural network; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5108
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.