Urbanization in most developing countries is gradually increasing. Quantitative estimation of urbanization to a standardized level is crucial for analysing the relation between urbanization and other factors, since urban growth affects diverse and complex natural and socio-economic phenomena. Spatially and spectrally, remotely sensed imageries covering urban areas display different characteristics from those of rural or mountainous areas. A three-dimensional discrete wavelet transform (3D DWT) scheme is regarded as useful for analysing both spatial and spectral information. In addition, the energy parameter of wavelet coefficient by 3D DWT indicates channels regarding dominant spatial and spectral frequency, extracted from the original image sets. Based on these spatial and spectral features and the energy parameter of 3D wavelet coefficients, three kinds of urban complexity index are proposed. Of the three indices, the third index is the most useful to quantitatively interpret urban complexity. These indices can be used for a basis to analyse the relationship between feature groups within urban environments.

Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-based image processing algorithms by comparing the performance of a single virtual server and multiple auto-scaled virtual servers under identical experimental conditions. In this study, the cloud computing environment is built with OpenStack, and four algorithms from the Orfeo toolbox are used for practical geo-based image processing experiments. The auto-scaling results from all experimental performance tests demonstrate applicable significance with respect to cloud utilization concerning response time. Auto-scaling contributes to the development of web-based satellite image application services using cloud-based technologies.

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

This study conducted multi-sensor image classification by utilizing Google Earth Engine (GEE) and applying satellite imagery from Korean Multi-purpose Satellite 3 (KOMPSAT-3), KOMPSAT-5 SAR, Compact Advanced Satellite 500-1 (CAS500-1), Sentinel-1, and Sentinel-2 within GEE. KOMPSAT-3/5 and CAS500-1 images are not provided by GEE. The land-use and land-cover (LULC) classification was performed using the random forest (RF) algorithm provided by GEE. The study experimented with 10 cases of various combinations of input data, integrating Sentinel-1/-2 imagery and high-resolution imagery from external sources not provided by GEE and those normalized difference vegetation index (NDVI) data. The study area is Boryeong city, located on the west coast of Korea. The classified objects were set to six categories, reflecting the region’s characteristics. The accuracy of the classification results was evaluated using overall accuracy (OA), the kappa coefficient, and the F1 score of the classified objects. The experimental results show a continued improvement in accuracy as the number of applied satellite images increased. The classification result using CAS500-1, Sentinel-1/-2, KOMPSAT-3/5, NDVI from CAS500-1, and NDVI from KOMPSAT-3 achieved the highest accuracy. This study confirmed that the use of multi-sensor data could improve classification accuracy, and the high-resolution characteristics of images from external sources are expected to enable more detailed analysis within GEE.

The fast-growing technological trends influencing Internet service businesses, cloud computing and mobile platforms are opening new opportunities for implementing and deploying geo-spatial applications. In particular, there are numerous variations or extensions of cloud computing services running on mobile devices. As for the geo-based application services, mobile cloud computing is at an early stage of research and development. The main goal of this study is to present a test case for geo-based image processing functions in the mobile cloud environment. Geo-based image processing algorithms for the mean difference change detection, maximum auto-correlation factor and threshold computation were used as the test case on an iPad in the Amazon web service cloud environment, and the processed results with real data sets are presented in this letter. It is suggested that mobile services with the geo-based image processing functions that fulfil user requirements in a cloud platform can provide opportunities to widen the range of remote-sensing applications.

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.