• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.153-162
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• 2004

In the summer of 1998-2001, a huge flood occurred in the Yangtze River in the eastern China. Low salinity water less than 28 psu from the river was detected around the southwestern part of the Jeju Island, which is located in the southern part of the Korean Peninsula. We studied how to detect low salinity water from the Yangtze River, that cause a terrible damage to the Korean fisheries. We established a relationships between low salinity at surface, turbid water from the Yangtze River and digital ocean color remotely sensed data of SeaWiFS sensor in the northern East China Sea, in the summer of 1998, 1999, 2000 and 2001. The salinity charts of the northern East China Sea were created by regeneration of the satellite ocean color data using the empirical formula from the relationships between in situ low salinity, in situ measured turbid water with transparency and SeaWiFS ocean color data (normalized water leaving radiance of 490 nm/555 nm).

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.753-760
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• 2011

The satellite Chollian was successfully launched on June 27, 2010 and is expected to perform its communication, oceanographic, and meteorological duties for seven years. The follow-up launch of the Chollian satellite is already being planned, and diverse studies are under way to enable the use of the Korean satellite data. Studies are also being actively conducted in and out of Korea to visualize the meteorological data on the open-source virtual globes. The meteorological data include ground observation, satellite, and digital-model data. In this study, an efficient three-dimensional technique was developed to visualize typhoons on the virtual globes using the Chollian satellite data. This study was conducted to provide service to the public via the scientific visualization of the satellite image data, and to create an efficient satellite image analysis environment for meteorological researchers.

• Proceedings of the KSRS Conference
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• v.1
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.325-333
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• 2010

Image registration involves overlapping images of an identical region and assigning the data into one coordinate system. Image registration has proved important in remote sensing, enabling registered satellite imagery to be used in various applications such as image fusion, change detection and the generation of digital maps. The image descriptor, which extracts matching points from each image, is necessary for automatic registration of remotely sensed data. Using contrast enhancement algorithms such as histogram equalization and image stretching, the normalized data are applied to the image descriptor. Drawing on the different spectral characteristics of high resolution satellite imagery based on sensor type and acquisition date, the applied normalization method can be used to change the results of matching interest point descriptors. In this paper, the matching points by scale invariant feature transformation (SIFT) are extracted using various contrast enhancement algorithms and injection of Gaussian noise. The results of the extracted matching points are compared with the number of correct matching points and matching rates for each point.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1039-1041
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• 2003

Ocean color products derived from remote sensing satellite data are useful for monitoring the sea water quality such as the concentrations of chlorophyll, sediments and dissolved organic matter. Currently, ocean color products derived from MODIS data can be requested from NASA over the internet. However, due to the bandwidth limitation of most users in this region, and the time delay in data delivery, the products cannot be use for near-real time monitoring of sea water chlorophyll. CRISP operates a MODIS data receiving station for environmental monitoring purposes. MODIS data have been routinely received and processed to level 1B. We have adapted the higher level processing algorithms from the Institutional Algorithms provided by NASA to run in a standalone environment. The implemented algorithms include the MODIS ocean color algorithms. Seasonal chlorophyll concentration composite can be compiled for the region. By comparing the near-real time chlorophyll product with the seasonal composite, anomaly in chlorophyll concentration can be detected.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1346-1348
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• 2003

In this paper, the interferometric coherence was estimated and the amplitude intensity was extracted using the repeat-pass interferometric data, acquired by European Remote Sensing Satellite 1 and 2. Then discrimination and classification of surface land types in Zhangjiakou test site, Hebei Province were carried out based on the coherence estimation and the intensity extraction. Seven types of land were discriminated and classified, including in two different types of meadows, woodland, dry land, grassland, steppe and water body. The backscatter and coherence characteristics of these land types on the multi-temporal images were analyzed, and the change of surface features with time series was also discussed.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.737-740
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• 2005

Soil moisture, through its dominance in the exchange of energy and moisture between the land and atmosphere, plays a crucial role in influencing atmospheric circulation. To identify the crucial role, it is a common agreement that knowledge of land surface processes and development of remote sensing techniques are of great important scientific issues. This research uses TRMM satellite C band (10.65 GHz) data to retrieve soil moisture on the Tibetan Plateau in Mainland China. Two retrieval schemes that are implemented include the t-(J) model and the R model. The latter one is developed based on a land surface process and radiobrightness (R) model for bare soil and vegetated terrain. Compared with the in situ ground measurements, the soil moisture retrieved from the R model and the t-(J) model with vegetation information obviously appear more accurate than that derived from bare soil model. Retrieved soil moisture contents from the two inversion models, R model and t-(J) model, have a similar trend, but the former appears to be superior in terms of correlation coefficient and bias compared with in situ data. In the future, we will apply the R model with the TRMM 10.65 GHz brightness temperature to monitor long-term soil moisture variation over Tibet Plateau.

• Korean Journal of Remote Sensing
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• v.5 no.1
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• pp.29-39
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• 1989

A study to develop generalized and systematically designed satellite image processing software system on mainframe computer was successfully carried out. Commercially available softwares such as LARSYS were analyzed and modified, and well known satellite data processing algorithms were implemented into comprehensive software. New algorithms were also presented and developed. The contents of developed softwere system may be divided into 8 major sections: menu and user interface, data file management, preprocessing, enhancement in monochrome image, multi-dimension image analysis, scene classification, image display/hardcopy, image handle utility software. Some additional software such as GIS and DBMS will make this software more comprehensive and generalized one for the satellite data processing.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.561-563
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• 2003

This study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including LANDSAT +ETM, KOMPSAT-1 EOC, ASTER VNIR and IKONOS panchromatic (Pan) and multi-spectral (M/S) images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by Ministry of Construction & Transportation based on NGIS (National Geographic Information System) and Ministry of Environment based on satellite remote sensing data. The results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.119-124
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• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.