• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1275-1277
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• 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 'reat Investigation of Land and Natural Resources of China' program.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1442-1444
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• 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
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• 2003.11a
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• pp.178-181
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• 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 Korean Society of Crop Science Conference
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• 2005.08a
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• pp.22-46
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• 2005

Spatial and timely information on crop and filed conditions is one of the most important basics for rational and efficient planning and management in agriculture. Remote sensing, GIS, and modeling are powerful tools for such applications. This paper presents an overview of the state of the art in remote sensing of crop and field conditions with some case studies. It is also shown that a synergistic linkage between process-based models and remote sensing signatures enables us to estimate the multiple crop/ecosystem variables at a dynamic mode. Remotely sensed information can greatly reduce the uncertainty of simulation models by compensating for insufficient availability of data or parameters. This synergistic approach allows the effective use of infrequent and multi-source remote sensing data for estimating important ecosystem variables such as biomass growth and ecosystem $CO_2$ flux. This paper also shows a geo-spatial information system that enables us to integrate, search, extract, process, transform, and calculate any part of the data based on ID#, attributes, and/or by river-basin boundary, administrative boundary, or boundaries of arbitrary shape/size all over Japan. A case study using the system demonstrates that the nitrogen load from fertilizer was closely related to nitrate concentration of groundwater. The combined use of remote sensing, GIS and modeling would have great potential for various agro-ecosystem applications.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1397-1399
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• 2003

When and Where, What kind of agricultural products will be produced and provided for the market? It is a commercial requirement, and also an academic questions to remote sensing technology. Crop physiology analysis and growth monitoring are important elements for precision agriculture management. Remote sensing technology supplies us more selections and available spaces in this dynamic change study by producing images of different spatial, spectral and temporal resolutions. Especially, the hyperspectral remote sensing should do play a key role in crop growth investigation at national, regional and global scales. In the past five years, Chinese academy of sciences and Japan NTT-DATA have made great efforts to establish a prototype information service system to dynamically survey the vegetable planting situation in Nagano area of Japan mainly based on remote sensing data. For such concern, a flexible and light-duty flight system and some practical data processing system and some necessary background information should be rationally made together. In addition, some studies are also important, such as quick pre-processing for hyperspectral data, Multi-temporal vegetation index analysis, hyperspectral image classification in support of GIS data, etc. In this paper, several spectral data analysis models and a designed airborne platform are provided and discussed here.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.739-744
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• 2002

Recently we have discovered that sediments should be separated from lithosphere, and soil should be separated from biosphere, both sediment and soil will be mixed sediments-soil-sphere (Seso-sphere), which is using particulate mechanics to be solved. Erosion and sediment both are moving by particulate matter with water or wind. But ancient sediments will be erosion same to soil. Nowadays, real soil has already reduced much more. Many places have only remained sediments that have ploughed artificial farming layer. Thus it means sediments-soil-sphere. This paper discusses sediments-soil-sphere erosion modeling. In fact sediments-soil-sphere erosion is including water erosion, wind erosion, melt-water erosion, gravitational water erosion, and mixed erosion. We have established geographical remote sensing information modeling (RSIM) for different erosion that was using remote sensing digital images with geographical ground truth water stations and meteorological observatories data by remote sensing digital images processing and geographical information system (GIS). All of those RSIM will be a geographical multidimensional gray non-linear equation using mathematics equation (non-dimension analysis) and mathematics statistics. The mixed erosion equation is more complex that is a geographical polynomial gray non-linear equation that must use time-space fuzzy condition equations to be solved. RSIM is digital image modeling that has separated physical factors and geographical parameters. There are a lot of geographical analogous criterions that are non-dimensional factor groups. The geographical RSIM could be automatic to change them analogous criterions to be fixed difference scale maps. For example, if smaller scale maps (1:1000 000) that then will be one or two analogous criterions and if larger scale map (1:10 000) that then will be four or five analogous criterions. And the geographical parameters that are including coefficient and indexes will change too with images. The geographical RSIM has higher precision more than mathematics modeling even mathematical equation or mathematical statistics modeling.

• Proceedings of the KSRS Conference
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• v.2
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• pp.884-887
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• 2006

This paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing, and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps, and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub-basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 minutes, considering rainfall duration, peak discharge, and flooding in the Yeongdeok basin.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.