Abstract
The majority of cloud applications are created or delivered to provide users with access to system resources or prebuilt processing algorithms for efficient data storage, management, and production. The number of cases linking cloud computing to the use of global observation satellite data continues to rise, owing to the benefits of cloud computing. This study aims to develop a cloud software as a service (SaaS) that yields reflectance products in high-resolution Korea Multi-Purpose Satellite (KOMPSAT)-3/3A satellite images. The SaaS model was designed as three subsystems: a Calibration Processing System (CPS), a Request System for CPS supporting RESTful application programming interface (API), and a Web Interface Application System. Open-source components, libraries, and frameworks were used in this study’s SaaS, including an OpenStack for infrastructure as a service. An absolute atmospheric correction scheme based on a Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code with atmospheric variable inputs was used to generate the top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The SaaS implemented in this study provides users with the absolute atmospheric calibration functionality to apply their KOMPSAT-3/3A satellite image set through a web browser and obtain output directly from this service. According to experiments to check the total performance time for images, bundled with four bands of red, green, blue, and near-infrared, it took approximately 4.88 min on average for the execution time to obtain all reflectance results since satellite images were registered into the SaaS. The SaaS model proposed and implemented in this study can be used as a reference model for the production system to generate reflectance products from other optical sensor images. In the future, SaaS, which offers professional analysis functions based on open source, is expected to grow and expand into new application fields for public users and communities.
Highlights
The concept of cloud computing arose from the idea of renting rather than buying computer resources such as hardware and software
The service model is classified as software services of software as a service (SaaS): platform as a service (PaaS), which provides the foundation for running applications, and infrastructure as a service (IaaS), which provides memory, CPU, storage, and network, depending on what elements apply to the cloud service
Users on the web can access the SaaS as the implemented results to provide the application to generate TOA and TOC reflectance products at the website
Summary
The concept of cloud computing arose from the idea of renting rather than buying computer resources such as hardware and software. The service model is classified as software services of software as a service (SaaS): platform as a service (PaaS), which provides the foundation for running applications, and infrastructure as a service (IaaS), which provides memory, CPU, storage, and network, depending on what elements apply to the cloud service. Many attempts have been made to link and apply cloud computing environments in managing, storing, and distributing large-capacity geo-observation satellite information. In the side of the business for earth observation (EO) image, the beneficial points of a cloud computing scheme or environment to an EO data processing system are as follows [3,4]: scalable image processing capabilities in online or on-demand mode, processors for effective colocation handling actual image databases, the software uses optimized for hardware.
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