• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.75-81
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• 2014

Until recently, photogrammetric applications for processing the satellite images and remotely sensed data have been used in different structure of functions and interfaces for sensor modeling by each developer. Thus, a standardized utilization procedure was necessary to solve the problems, such as expandability, cost, inefficiency of sources which were resulted from different approaches. Therefore, National Geospatial Intelligence Agency (NGA) provided unified interfaces by developing Community Sensor Model (CSM) to sensor models in same way. In this study, we suggested the method of design and analyzed main functions needed modeling for the frame sensor using CSM Application Program Interface (API) provided by NGA. We also applied the designed structure to the modeling. The implemented CSM was verified by groundToImage and imageToGround. In the future, the active R&D is expected with using CSM due to the cost saving effect of software development and remarkable expandability of sensor.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.657-672
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• 2016

Coastal erosion happens frequently in many different types. To control coastal erosion zone effectively and establish response plans, we need to accumulate data indicating topography changes through monitoring the erosion situation continuously. UAV photogrammetric systems, which can fly autonomously at a low altitude, are recommended as an economical and precision means to monitor the coastal zones. In this study, we aim to verify the accuracy of the generated orthoimages and DEM as a result of processing the UAV data of a coastal zone by comparing them with various reference data. We established a verification routine and examined the possibilities of applying the UAV photogrammetric systems to monitoring coastal erosion by checking the analyzed accuracy by the routine. As a result of verifying the generated the geospatial information from acquired data under various configurations, the horizontal and vertical accuracy (RMSE) were about 2.7 cm and 4.8 cm respectively, which satisfied 5 cm, the accuracy required for coastal erosion monitoring.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.275-284
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• 2006

The noise sources of a Hyperion image are mainly due to the atmospheric effects, the sensor's instrumental errors, and A/D conversion. Though uncalibrated, overlapping, and all deep water absorption bands generally are removed, there still exist noisy bands. The visual inspection for selecting clean and stable processing bands is a simple practice, but is a manual, inefficient, and subjective process. In this paper, we propose that the extrema ratio be used for noise estimation and unsupervised band selection. The extrema ratio was compared with existing SNR and entropy measures. First, Gaussian, salt and pepper, and Speckle noises were added to ALI (Advanced Land Imager) images with relatively low noises, and the relation of noise level and those measures was explored. Second, the unsupervised band selection was performed through the EM (Expectation-Maximization) algorithm of the measures which were extracted from a Hyperion images. The Hyperion data were classified into 5 categories according to the image quality by visual inspection, and used as the reference data. The experimental result showed that the extrema ratio could be used effectively for band selection of Hyperion images.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.447-456
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• 2003

This study investigated the method for obtaining 3-dimensional terrain information on inaccessable areas by evaluating geometric accuracy of the EOC pass image and scene image acquired from the KOMPSAT-1 satellite. For this purpose, the following four experiments were conducted to evaluate the accuracy of the KOMPSAT-1 EOC satellite data. 1) Calculation of ground coordinates by using ancillary data and image coordinates on Level 1R that were processed by the pre-processing system of KOMPSAT-1. 2) Calculation of 3-dimensional ground coordinates from the ground coordinates of stereo images calculated by using ancillary data, based on space intersections. 3) Execution of bundle adjustment by using GCP (Ground Control Point) extracted in a part of the stereo pass image (KOMPSAT-1 EOC, 1 scene size); and then, evaluation of the ground coordinates from the calculated exterior orientation. 4) Evaluation of accuracy by applying the exterior orientation calculated from 3) To the whole pass image.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.137-147
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• 2014

We proposed and verified the methods to maintain data qualities as well as to reduce data volume for the Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in geostationary orbit. For the GOCI level-2 data, 92.9% of data volume could be saved by only the data compression. For the GOCI level-1 data, however, just 20.7% of data volume could be saved by the data compression therefore another approach was required. First, we found the optimized number of bits per a pixel for the GOCI level-1 data from an idea that the quantization bit for the GOCI (i.e. 12 bit) was less than the number of bits per a pixel for the GOCI level-1 data (i.e. 32 bit). Experiments were conducted using the $R^2$ and the Modulation Transfer Function (MTF). It was quantitatively revealed that the data qualities were maintained although the number of bits per a pixel was reduced to 14. Also, we performed network simulations using the Network Simulator 2 (Ns2). The result showed that 57.7% of the end-toend delay for a GOCI level-1 data was saved when the number of bits per a pixel was reduced to 14 and 92.5% of the end-to-end delay for a GOCI level-2 data was saved when 92.9% of the data size was reduced due to the compression.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.2
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• pp.85-92
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• 1989

The aim of this study is to find out the combination of effective transformed data, applying Remote Sensing techniques, as to the classification and particular objects by transforming the MSS data and TM data of the satellite LANDSAT into several linearly transformed data. Since one of the problems in the processing of the LANDSAT data is the vastness of the data, the Linear Transformation could be a method to perform analysis of those vast data, more efficiently and economically. This method is carried out as follows : (1) offering the simplicity over complex data, (2) selectional processing over redundant data and removing unnecessary data, (3) emphasizing on the object of the study ; by transforming multispectral data through linear calculation and statistical transformation. In this study, the analysis and transformation of the data have been performed by means of Band Ratioing and Principal Component Analysis. As the classificatory consequence, Infrared/RED Ratioing which expands the characterization of green vegetation, has been useful for a distinctive classification among other classes. For the Principal Component Analysis, band 1,2,7 are efficient in the classification of the green vegetation.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1233-1242
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• 2017

In this study, we tried to analyze deforestation due to forest fire by using KOMPSAT satellite imagery. For deforestation analysis, unsupervised change detection algorithm is applied to multitemporal images. Through ITPCA (ITerative Principal Component Analysis) of NDVI (Normalized Difference Vegetation Index) generated from multitemporal satellite images before and after forest fire, changed areas due to deforestation are extracted. In addition, a post-processing method using SRTM (Shuttle Radar Topographic Mission) data is involved in order to minimize the error of change detection. As a result of the experiment using KOMPSAT-2 and 3 images, it was confirmed that changed areas due to deforestation can be efficiently extracted.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.17-36
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• 1998

The Radon transform has been widely used in various techniques of digital image processing such as the computerized topography, lineament analysis in a remotely sensed image, slant-stack processing of seismic data, and so on. Compared to the Fourier transform, the utility of two-dimensional convolutional or correlational properties of the Radon transform, however, has been underestimated. We show that the two-dimensional convolution and correlation is respectively reduced to be one-dimensional convolution and correlation with respect to ρ in the Radon space. Therefore, one can achieve a two dimensional filtering by applying a simple one-dimensional convolution in the Radon space followed by an inverse Radon transform. Tests of the approach using FIR filters are carried out specifically for enhancing the ship wake in a RADARSAT SAR image. The test results demonstrate that the two-dimensional filtering through the Radon transform effectively enhance the ship wake features as well as reducing sea speckle in the image. Although two-dimensional convolution and correlation through the Radon transform are not so much useful as those through the courier transform in views of efficiency and effectiveness, it can be utilized to improve the quality of a digitally processed output when the process should be accompanied by the Radon transform such as topography and lineament analysis of SAR image.