• Korean Journal of Remote Sensing
• /
• v.27 no.6
• /
• pp.761-771
• /
• 2011

The sensitivity of microwave scattering to the dielectric properties and the geometric structure of soil surfaces makes radar remote sensing a challenge for a wide range of environmental issues directly related to the condition of natural surfaces. Especially, the potential for retrieving soil moisture with a high spatial and/or temporal resolution represents a significant contribution to hydrological and ecological modeling. This paper aims to review the current state of the art in SAR technology and methodological issues towards the discovery of a new potential accurate monitoring of soil moisture changes. In this paper, important parameters or constraints significantly affect the sensitivity of the measurements to soil moisture, such as roughness statistics, spatial resolution, and local topography, are discussed to improve the applicability of SAR remote sensing techniques. This study particularly intends to discuss important notes for developing smart and reliable methods capable of retrieving geophysical information.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1442-1444
• /
• 2003

This paper presents a geological remote sensing information extraction system, the aim of which is to provide practical models and powerful tools to extract geological information from remote sensing images for geological exploration applications. After reviewing and analyzing the existing methods for geological information extraction, we developed more than ten models to enhance and extract geological information, such as alteration information, linear features and special lithological characters. The system is developed based on Erdas Imagine using its programming language. It has been successfully used in the ‘Great Investigation of Land and Natural Resources of China’ program.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.34-37
• /
• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1581-1587
• /
• 2022

Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.9 no.1
• /
• pp.97-107
• /
• 1991

This paper was suggested the detailed methods on the integration of Remote Sensing and GIS for various application of two functions at the one system with making the most use of respective merits rather than make use of independent systems. It developed of algorithm about simultaneous overlay of raster and vector data for remote sensing and GIS for these objects. For test application on integration of remote sensing and GIS, it used of remote sensing data of satellite and used to topographic map of the same area for vector data acquisition of GIS application. For the practical application, it proved of effective value of integration of raster and vector data by present of useful technique with multilateral approach method through data conversion about thematic application for major application fields of remote sensing and GIS and it suggested that new application technique for integrated application of remote sensing GIS through synthetic situation analysis.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.52 no.4
• /
• pp.393-402
• /
• 2007

Remote sensing data can be integrated into crop models, making simulation improved. A crop model that uses remote sensing data was evaluated for its capability, which was performed through comparing three different methods of canopy measurement for cotton(Gossypium hirsutum L.). The measurement methods used were leaf area index(LAI), hand-held remotely sensed perpendicular vegetation index(PVI), and satellite remotely sensed PVI. Simulated values of cotton growth and lint yield showed reasonable agreement with the corresponding measurements when canopy measurements of LAI and hand-held remotely sensed PVI were used for model calibration. Meanwhile, simulated lint yields involving the satellite remotely sensed PVI were in rough agreement with the measured lint yields. We believe this matter could be improved by using remote sensing data obtained from finer resolution sensors. The model not only has simple input requirements but also is easy to use. It promises to expand its applicability to other regions for crop production, and to be applicable to regional crop growth monitoring and yield mapping projects.

• Journal of Environmental Impact Assessment
• /
• v.4 no.1
• /
• pp.59-64
• /
• 1995

Environmental Impact Assesment(EIA) is composed of various procedures, such as screening, scoping, inventory survey, prediction, assessment, mitigation measure, alternative assessment, and post management. Remote sensing introduced lately begins to be applied ecosystem and land use in inventory survey and assessment of EIA. This study explains on land use classification, buffering analysis of residential area, and overlaying analysis of odor predictive data with residential area for application to EIA with remote sensing data. Residential area extracted from land use classification of remote sensing provides effectively buffering analysis of residential area in selection of landfill site with GIS. It could assess also residential effect to an offensive odor by overlaying analysis. Application methods in EIA should be enlarged to assess effectively.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.178-181
• /
• 2003

The temporal GIS data modeling methods are discussed in this paper. At first, two conceptual models of spatio-temporal data are introduced, and then some typical STDMs based on these two models are summed up and compared. After that, the spatio-temporal changes are analyzed thoroughly, and then how to model spatio -temporal data from different aspects is discussed. At last, several issues that need further research are pointed out.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.132-134
• /
• 1999

Rainfall characteristics for designing the optimum millimeter-wave communication systems from two rainfall data set was analyzed. Two rainfall data sets were compared; one-minute rainfall rate data, one-hour synoptic observation data. Each data set has different observation method, sampling frequency. We looked for tendency and quality confluence between two data sets. We showed several results using one-minute rainfall data by millimeter-wave attenuation model. A climatological one-minute rainfall rate data set over Korean Peninsula will be made after data quality control procedure

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.170-173
• /
• 2006

The remote sensing is powerful oceanographic tools not only for Integrated Coastal Zone Management (ICZM) but also for various areas of oceanography. Thank to effort of Government and local authorities as well as active support of international institutions, many projects on the applied oceanography had and have been caring out in coastal and offshore waters of Vietnam sea. One of the modern methods which has been used in these project is ocean color remote sensing technique. This paper will present some preliminary results obtain from application of these techniques in study of coastal and offshore environment of Vietnam sea.