A grid enabled Monte Carlo hyperspectral synthetic image remote sensing model (GRID-MCHSIM) for coastal water quality algorithm

Abstract

Previous studies indicate that parallel computing for hyperspectral remote sensing image generation is feasible. However, due to the limitation of computing ability within single cluster, one can only generate three bands and a 1000*1000 pixels image in a reasonable time. In this paper, we discuss the capability of using Grid computing where the so-called eScience or cyberinfrastructure is utilized to integrate distributed computing resources to act as a single virtual computer with huge computational abilities and storage spaces. The technique demonstrated in this paper demonstrates the feasibility of a Grid-Enabled Monte Carlo Hyperspectral Synthetic Image Remote Sensing Model (GRID-MCHSIM) for coastal water quality algorithm.

Optimal band selection in hyperspectral remote sensing of aquatic benthic features: a wavelet filter window approach

“…As indicated previously, the optimal band selection technique can be used to operate upon synthetic hyperspectral signatures derived from radiative transfer models 6,7,8,9,10 . When the band selection technique is applied to modeled signatures, the results represent, in essence, a hyperspectral imaging spectroscopy inversion solution.…”

eScience for molecular-scale simulations and the e Minerals project

“…The examples we have described here focus on applications to molecular simulation sciences, and the breadth of these examples (different scientific issues, different methods and different resource demands) demonstrates the generality of the approach. We have also exploited this work for quite different applications within environmental sciences, including applications in remote sensing and hydrology (Chiang et al (2006(Chiang et al ( , 2007, and work to be published). Figure 7.…”