• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.287-296
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• 2012

Although low spatial resolution satellite images like MODIS and GOCI can be important to observe land surface, it is often difficult to visually interpret the imagery because of the low contrast by prevailing cloud covers. We proposed a simple and adaptive stretching algorithm to enhance image contrast over land areas in cloudy images. The proposed method is basically a linear algorithm that stretches only non-cloud pixels. The adaptive linear stretch method uses two values: the low limit (L) from image statistics and upper limit (U) from low boundary value of cloud pixels. The cloud pixel value was automatically determined by pre-developed empirical function for each spectral band. We used MODIS and GOCI images having various types of cloud distributions and coverage. The adaptive contrast stretching method was evaluated by both visual interpretation and statistical distribution of displayed brightness values.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.411-414
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• 2005

In this study, 8 Landsat(TM/ETM+) satellite images acquired from 1984 to 2002 were used to investigate the growth of Chuncheon city, Kangwon-do, Korea. The images were geocoded and classified using training set collected from field survey. Four land-use types were classified such as urban area, green zone, agricultural land and water body. It also showed rapid increase of urban area in the past two decades from 1166ha in 1984 to 3358ha in 2002. About 2182ha of agricultural land and green zone have been changed to urban area. Agricultural land was newly formed from the green zone.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.2
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• pp.145-156
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• 1995

Satellite infrared images were analyzed to study the distribution of eddies off the east coast of Korea from 1987 to 1991. Most of the eddies were filament-type and were generated near the fringe of the East Korea Warm Current (EKWC) flowing northward. Eddies having length of 20-40km and width of 10-20km were most abundant. The meso-scale eddies of 100-200km in diameter were found between Mukho and Wonsan Bay in almost all the images. There was no evidence for the consistent movement of eddies to a definite direction. The Ulleung Warm Eddy, although reported previously by the hydrographic data, could not be identified by the limited amount of infrared imagery.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.343-348
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• 1998

In this study, we extracted several terrain information using satellite and aerial images. We detected change of terrain using Landsat Thematic Mapper(TM) and aerial images which are multitemporal data. In change detection processing, we first classified satellite images by ISODATA algorithm which is an unsupervised learning algorithm, then performed change detection. By this method, we could obtain good result. Also we introduce sub-pixel concept to classify road and agriculture area in inaccessible area. In summary, in chang detection processing, we can find that the used method is efficient.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.79-88
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• 2017

The RapidEye satellite sensor has various spectral wavelength bands, and it can capture large areas with high temporal resolution. Therefore, it affords advantages in generating various types of thematic maps, including land cover maps. In this study, we applied a supervised classification scheme to generate high-resolution land cover maps using RapidEye images. To improve the classification accuracy, object-based classification was performed by adding brightness, yellowness, and greenness bands by Tasseled Cap Transformation (TCT) and Normalized Difference Water Index (NDWI) bands. It was experimentally confirmed that the classification results obtained by adding TCT and NDWI bands as input data showed high classification accuracy compared with the land cover map generated using the original RapidEye images.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.479-482
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• 2003

Landsat-TM images were applied for evaluating the spatial variations of flow and water quality at the Saemankeum areas. For supervised classifications, the results from hydrodynamic modeling and water quality data were compared to the reflectance characteristics of the satellite images. Multiple regression analyses indicated that suspended sediment, transparency, salinity, total nitrogen, and total phosphorus showed a good relationship to the signature. Supervised classifications showed spatial variations of the water environments at the areas under construction. The results showed the satellite imagery may be applied for the project areas with a reasonable degree of accuracy.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.240-242
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• 2008

In the present article, we analyze airborne Pi-SAR (Polarimetric-Interferometric SAR) X-band images of ocean waves around the Miyake Island at approximately 180 km south from Tokyo, Japan. Two images of a same scene were produced at approximately 40 min. interval from two directions at right angles. One image shows dominant range travelling waves, but the other image shows a different wave pattern. This difference can be caused by the different image modulations of RCS and velocity bunching. We have estimated the dominant wavelength from the image of range waves, and from the wave phase velocity computed from the dispersion relation (though no wave height data were available), the image intensity is computed by using the velocity bunching model. The comparison of the result with the second image at right angle strongly suggests the evidence of velocity bunching.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.53-56
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• 2008

Generally, the quality of satellite images is expressed by GSD (Ground Sample Distance), MTF (Modulation Transfer Function) and SNR (Signal to Noise Ratio). However, these factors are technology-oriented and do not explain interpretability of satellite images. We need a standardized index which shows standard of interpretability. In this study, we estimated NIIRS (National Imagery Interpretability Rating Scale) through the GIQE (General Image Quality Equation) which is able to judge image interpretability with the standardized index. Traditionally, NIIRS has been determined manually by specialized image analysts. We used the GIQE in order to reduce inefficiency and high costs cause by manual interpretation and to produce accurate NIIRS. For monitoring image degradation, we estimated GIQE physical parameters from image analysis and carried out time series analysis about the quality of the KOMPSAT-1 images. On all of the tests, we were able to identify the image degradation due to the changing time. This indicates that NIIRS derived from GIQE will be used for image degradation indicator.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.134-137
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• 1998

NOAA satellite data and GMS data have been received at the Institute of Industrial Science, University of Tokyo since early 80's and 1994, respectively. So far, all data are archived and users can look their quick look images through the Internet and get the data by request. The following processed data set will be available soon with the corporation with the Science University of Tokyo: Radiometrically corrected by 65 code and geometrically corrected NOAA data with the corporation with Iwate University and NDVI, SST and cloud classified images as their products. 1 km AVHRR Land Project Data Set of Asia and their 14 regional subsets. Geometrically corrected GMS images and surface temperature maps, sea surface temperature maps and cloud classification maps.

• Journal of Satellite, Information and Communications
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• v.6 no.2
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• pp.41-45
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• 2011

This paper presents a fire detection criterion based on flame color, spatial and temporal characteristic analysis of color fire images. To propose the criterion, Firstly the fire candidate regions were selected by using analyzed Cr and Y threshold value, and then texture analysis of candidate regions was performed by using DCT. Finally variation of Y values of these regions was calculated for temporal analysis. The proposed fire detection criterion was simulated by using fifteen test images and practicality was verified.