• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.817-827
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• 2014

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. This study proposes a maximum a posteriori (MAP) estimation using Point-Jacobian iteration to restore a degraded image. The proposed method assumes a Gaussian additive noise and Markov random field of spatial continuity. The proposed method employs a neighbor window of spoke type which is composed of 8 line windows at the 8 directions, and a boundary adjacency measure of Mahalanobis square distance between center and neighbor pixels. For the evaluation of the proposed method, a pixel-wise classification was used for simulation data using various patterns similar to the structure exhibited in high resolution imagery and an unsupervised segmentation for the remotely-sensed image data of 1 mspatial resolution observed over the north area of Anyang in Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution imagery.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.383-396
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• 2004

The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• Proceedings of the KSRS Conference
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• v.1
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• pp.130-133
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• 2006

Three scatterometers will be launched by Europe, India, and China in the next few years and they will fly in tandem with QuikSCAT of the United States. The potential improvement in coverage will open up new operational and research applications.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.9-14
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• 1999

This paper describes an algorithm for the correction of major radiometric errors shown in MEIS (Multi-spectral Earth Imaging System) images on board KITSAT-3. MEIS images contain various radiometric errors as also shown in the images obtained from other remote sensing sensors. This paper introduces the two major radiometric error sources shown in MEIS images and the corresponding correction algorithm. The proposed algorithm was integrated to an operational preprocessing software and validated by applying the algorithm to several tens of MEIS images. This algorithm will therefore applied operationally to raw MEIS images before they are distributed to users.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.127-139
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• 2018

The GOCI atmospheric correction overland surfaces is essential for the time-series analysis of terrestrial environments with the very high temporal resolution. We develop an operational GOCI atmospheric correction method over land surfaces, which is rather different from the one developed for ocean surface. The GOCI atmospheric correction method basically reduces gases absorption and Rayleigh and aerosol scatterings and to derive surface reflectance from at-sensor radiance. We use the 6S radiative transfer model that requires several input parameters to calculate surface reflectance. In the sensitivity analysis, aerosol optical thickness was the most influential element among other input parameters including atmospheric model, terrain elevation, and aerosol type. To account for the highly variable nature of aerosol within the GOCI target area in northeast Asia, we generate the spatio-temporal aerosol maps using AERONET data for the aerosol correction. For a fast processing, the GOCI atmospheric correction method uses the pre-calculated look up table that directly converts at-sensor radiance to surface reflectance. The atmospheric correction method was validated by comparing with in-situ spectral measurements and MODIS reflectance products. The GOCI surface reflectance showed very similar magnitude and temporal patterns with the in-situ measurements and the MODIS reflectance. The GOCI surface reflectance was slightly higher than the in-situ measurement and MODIS reflectance by 0.01 to 0.06, which might be due to the different viewing angles. Anisotropic effect in the GOCI hourly reflectance needs to be further normalized during the following cloud-free compositing.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.109-120
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• 1994

The study is aimed at the extraction of the groundwater potential areas utilizing the remotely sensed data from satellites. The results of the study are summarized as follows. Analyses of the existing operational wells for groundwater supply indicate that 81% of them are related with lineaments and 51% of them are located at the intersections of lineameters. Thus the features of lineaments are considered to be one of the most important parameters to extract a high potertial area of groundwater. Taking into acount features of lineament, high potential points were extracted from Landsat TM data based on the theory developed in this research, then verifications were made through actual drilling. The result of verification indicates that 9 points produces more 200 cubic meter/day which is the amount required from economical point of view for an operational use. Since the actual boring was not made on the recommended points for 4 points due to the difficulty of access to the exact points and of the approval for boring, they did not yield enough output. The result might have been improved if the exact points were bored and if the boring bad been made deeper, since the maximum depth of boring was limited to 62 meters.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.201-203
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• 2003

Two MODIS instruments on board the Terra and Aqua Satellites are operational for global remote sensing of the land, ocean and atmosphere. Atmospheric sounding data with a high spatial resolution from MODIS will provide a wealth of useful information. The vertical air temperature and moisture data were retrieved using the MODIS data, and compared with the radiosonde data obtained in the Korean Peninsula. The correlation coefficient are 0.99 and 0.89 for air temperature and moisture cases, respectively. Air temperature data were relatively good agreement, but the moisture data from MODIS were underestimated.

• Ocean and Polar Research
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• v.23 no.2
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• Korean Journal of Remote Sensing
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• v.19 no.1
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• pp.37-42
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• 2003

Recently, KARI implemented the receiving and processing system for MODIS sensor data from NASA EOS satellites (TERRA and AQUA). This paper shows the development strategy considered, system requirement derived, system design, characteristic and test results of processing system. System operation concept and sample image are also provided. Implemented system was proven to be fully operational through lots of pass operations activities from RF signal reception to level-1 processing.