• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.245-253
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• 2015

According to wide civilian utilization of multi sensor satellite information, practical needs for fusion processing and interoperable operation with multiple remote sensing imageries within distributed remote server are being increased. For this task, OGC standards with respect to satellite images and its derived products are crucial factors. This study is to present an applicability of WPS through testing implementation of image processing algorithm. Open sources such as Geoserver and OTB were used linked to WPS application for implementation. WPS can be solely used for web service supporting geoprocessing algorithm, but technical consideration compromising with other important standard protocols including WMS, WFS, WCS, or WMTS is necessary to build full featured geo web for remote sensing imageries. It is expected that application of these international standards for geo-spatial information is an important approach to produce value-added results by interoperable processing between interorganizations or information dissemination containing practical satellite image processing functionalities.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.450-453
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• 2005

A forest fire occurs mainly as natural factor such as wind, temperature or human factor such as light. Recently, the most of forest fire prevention is prediction or prevision against forest fire by using remote sensing technology. However in order to forest fire prevention, the remote sensing has many limitations such as high cost and advanced technologies and so on. Therefore, we need to multisensor integration system that utilize not only remote sensing but also in-situ sensing in order to reduce large damage of forest fire though analysis of happen cause and prediction routing of occurred forest fire. In this paper we propose a multisensor integration system that offers prediction information of factors and route of forest fire by integrates collected data from remote sensor and in-situ sensor for forest fire prevention. The proposed system is based on wireless sensor network for collect observed data from various sensors. The proposed system not only offers great quality information because firstly, raw data level fuse different format of collected data from remote and in-situ sensor but also accomplish information level fusion based on result of first stage. Offered information from our system can help early prevention of factor and early prevision against occurred forest fire which transfer to SMS service or alert service into monitoring interface of administrator.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1341-1347
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• 2021

The need for an effective disaster management system has grown these days to protect public safety as the number of disasters causing massive damage increases. Since disaster-induced damage can develop in various ways, rapid and accurate countermeasures must be prepared soon after disasters occur. Numerous studies have continuously developed remote sensing and GIS (Geographic Information System)-based techniques for disaster monitoring and damage analysis. This special issue presents the research results on disaster prediction and monitoring based on various remote sensors on different platforms from ground to space and disaster management using GIS techniques. The developed techniques help manage various disasters such as storms, floods, and forest fires and can be combined to achieve an integrated and effective disaster management system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.694-696
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• 2003

The KOMPSAT/OSMI remote sending data of 800 km swath are collected by whisk broom method employing 96 charge coupled devices (CCDs). The stripping noise in the OSMI imageries, which arise mainly due to the non-uniform sensitivities of 96 CCDs, are the major hindrance for oceanographic applications of the OSMI data. The OSMI images are corrected by 'Ensemble Smoothness' method which is based on an assumption that the series of the averages and variances of digital numbers in each line should vary smoothly. The data of each line are corrected by linear regression model of which coefficients are obtained by Ensemble Smoothness method. Our algorithm can be applied not only to OSMI data but also for other remote sensing date collected by whisk broom or push broom.

• Proceedings of the KSRS Conference
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• v.2
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• pp.1003-1006
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• 2006

Frontal regions in midlatitude storms exhibit a wide range of behavior, which can be observed by remote sensors. These include decay, strengthening, rotating, and sometimes spawning of new cyclones. Here we refine and apply recent theories of front and frontal wave development to a case of a front clearly observed and analyzed in remote sensing data. By applying innovative analysis techniques to the data we assess the respective roles of ageostrophy, background deformation, and Boundary Layer processes in determining the evolution of the surface front. Our analysis comprises of diagnosis of the terms appearing in the vorticity and divergence equations using remotely sensed observations.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1206-1208
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• 2003

The extraction of earthquake damage from remote sensed imagery requires high spatial resolution and temporal effectiveness of acquisition of imagery. The analog photographs and visual interpretation were taken traditionally. Now it is possible to acquire damage information from many commercial high resolution RS satellites. The key techniques are processing velocity and precision. The authors developed the automatic / semiautomatic image process techniques including feature enhancement, and classification, designed the emergency Earthquake Damage and Losses Evaluate System based on Remote Sensing (RSEDLES). The paper introduced the functions of RSEDLES as well as its application to the earthquakes occurred recently.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.256-261
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• 1999

Global vegetation change is one of major global concerns. Remote sensing images provide an efficient and useful data source to estimate global vegetation covers, and a number of methods have been proposed to estimate them. Among them, the NDVI is one of the most popular indices, and it is_easy to calculate with simple image computing. However, this index is very much affected by the radiometric environment of sensing such as atmospheric conditions and the sun illumination angle. Therefore, it is not appropriate to apply the NDVI to investigate seasonal changes. This paper discusses these problems and proposes an alternative index, MODVI(Modified Vegetation Index), that is less affected by radiometric environment changes. An experiment was conducted to compare these two indices using temporal Landsat TM sub-scenes.

• Korean Journal of Remote Sensing
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• v.10 no.1
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• pp.15-29
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• 1994

Radiometric correction refers to variations in the data that are not caused by the object or scene being scanned. These variations can be caused by differing sensitivities of the detectors of the sensing system, malfunctioning detectors, or atmospheric interference. Radiometric corrections can be applied to correct for these variations, such as for differing sensitivities of detectors (causing striped image), for detectors (resulting in pixels with digital values of zero), or to correct for atmospheric bias due to scattering of radiation. This paper discussed and illustrated some of the important principles of the radiometric correction methods.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.721-730
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• 2010

In satellite remote sensing, irregular temporal sampling is a common feature of geophysical and biological process on the earth's surface. Lee (2008) proposed a feed-back system using a harmonic model of single period to adaptively reconstruct observation image series contaminated by noises resulted from mechanical problems or environmental conditions. However, the simple sinusoidal model of single period may not be appropriate for temporal physical processes of land surface. A complex model of multiple periods would be more proper to represent inter-annual and inner-annual variations of surface parameters. This study extended to use a multi-periodic harmonic model, which is expressed as the sum of a series of sine waves, for the adaptive system. For the system assessment, simulation data were generated from a model of negative errors, based on the fact that the observation is mainly suppressed by bad weather. The experimental results of this simulation study show the potentiality of the proposed system for real-time monitoring on the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather.