• Korean Journal of Geomatics
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• v.3 no.1
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• pp.59-65
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• 2003

The merging of different scales or multi-sensor image data is becoming a widely used procedure of the complementary nature of various data sets. Ideally, the merging method should not distort the characteristics of the high-spatial and high-spectral resolution data used. To present an effective merging method for survey of reclaimed land using the high-resolution (6.6 m) Electro-Optical Camera (EOC) panchromatic image of the first Korea Multi-Purpose Satellite 1 (KOMPSA T-l) and the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) image data, this paper compares the results of Intensity Hue Saturation (IHS) and Principal Component Analysis (PCA) methods. The comparison is made by statistical and visual evaluation of three-color combination images of IHS and PCA results based on spatial and spectral characteristics. The use of MODIS bands 1, 2, and 3 with a contrast stretched EOC panchromatic image as a substitute for intensity was found to be particularly effective in this study.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.135-139
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• 1999

Wind velocity is one of the primary variables for describing atmospheric state from GMS-5. And its accurate depiction is essential for operational weather forecasting and for initialization of NWP(Numerical Weather Prediction) models. The aim of this research is to incorporate imagery from other available spectral channels and examine the error characteristics of winds derived from these images. Multi spectral imagery from GMS-5 was used for this purpose and applied to Korean region with together BoM(Bureau of Meteorology). The derivation of wind velocity estimates from low and high resolution visible, split window infrared, and water vapor images, resulted in improvements in the amount and quality of wind data available for forecasting.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.581-590
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• 2014

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3907-3912
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• 2023

Radiation portal monitors (RPMs) installed at airports and harbors to prevent illicit trafficking of radioactive materials generally use large plastic scintillators. However, their energy resolution is poor and radionuclide identification is nearly unfeasible. In this study, to improve isotope identification, a RPM system based on a multi-array plastic scintillator and convolutional neural network (CNN) was evaluated by measuring the spectra of radioactive sources. A multi-array plastic scintillator comprising an assembly of 14 hexagonal scintillators was fabricated within an area of 50 × 100 cm². The energy spectra of ¹³⁷Cs, ⁶⁰Co, ²²⁶Ra, and ⁴K (KCl) were measured at speeds of 10-30 km/h, respectively, and an energy-weighted algorithm was applied. For the CNN, 700 and 300 spectral images were used as training and testing images, respectively. Compared to the conventional plastic scintillator, the multi-arrayed detector showed a high collection probability of the optical photons generated inside. A Compton maximum peak was observed for four moving radiation sources, and the CNN-based classification results showed that at least 70% was discriminated. Under the speed condition, the spectral fluctuations were higher than those under dwelling condition. However, the machine learning results demonstrated that a considerably high level of nuclide discrimination was possible under source movement conditions.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.319-324
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• 1998

Ocean Scanning Multispectral Imager (OSMI) is a payload on the Korean Multi-purpose SATellite (KOMPSAT) to perform worldwide ocean color monitoring for the study of biological oceanography. The instrument images the ocean surface using a whisk-broom motion with a swath width of 800 km and a ground sample distance (GSD) of < 1 km over the entire field-of-view (FOV). The instrument is designed to have an on-orbit operation duty cycle of 20% over the mission lifetime of 3 years with the functions of programmable gain/offset and on-board image data storage. The instrument also performs sun calibration and dark calibration for on-board instrument calibration. The OSMI instrument is a multi-spectral imager covering the spectral range from 400 nm to 900 nm using a CCD Focal Plane Array (FPA). The ocean colors are monitored using 6 spectral channels that can be selected via ground commands after launch. The instrument performances are fully measured for 8 basic spectral bands centered at 412nm, 443nm, 490nm, 510nm, 555nm, 670nm, 765nm and 865nm during ground characterization of instrument. In addition to the ground calibration, the on-board calibration will also be used for the on-orbit band selection. The on-orbit band selection capability can provide great flexibility in ocean color monitoring.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Journal of Astronomy and Space Sciences
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• v.17 no.2
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• pp.309-316
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• 2000

KOMPSAT-2 will carry MSC(Multi-Spectral Camera) which provides 1m resolution panchromatic and 4m resolution multi-spectral images at the altitude of 685km sun-synchronous mission orbit. The mission operation of KOMSPAT-2 is to provide the earth observation using MSC with nadir pointing. KOMPSAT-2 will also have the capability of roll/pitch tilt maneuver using reaction wheel of satellite as required. In order to protect MSC from thermal distortion as well as direct sunlight, MSC shall be operated within the constraint of sun incidence angle. It is expected that the sunlight will not violate the constraint of sun incidence angle for normal mission operations without roll/pitch maneuver. However, during roll/pitch tilt operations, optical module of MSC may be damaged by the sunlight. This study analyzed sun incidence angle of payload using KOMPSAT-2 AOCS (Attitude and Orbit Control Subsystem) Design and Performance Analysis Soft ware for KOMPSAT-2 normal mission operations.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Journal of the Korean Geographical Society
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• v.47 no.2
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• pp.282-296
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• 2012

This study compared effects of spatial resolution ratio in image fusion by Korea Multi-Purpose SATellite 2 (KOMPSAT II), also known as Arirang-2. Image fusion techniques, also called pansharpening, are required to obtain color imagery with high spatial resolution imagery using panchromatic and multi-spectral images. The higher quality satellite images generated by an image fusion technique enable interpreters to produce better application results. Thus, image fusions categorized in 3 domains were applied to find out significantly improved fused images using KOMPSAT 2. In addition, all fused images were evaluated to satisfy both spectral and spatial quality to investigate an optimum fused image. Additionally, this research compared Pixel-Based Image Analysis (PBIA) with the GEOgraphic Object-Based Image Analysis (GEOBIA) to make better classification results. Specifically, a roof top of building was extracted by both image analysis approaches and was finally evaluated to obtain the best accurate result. This research, therefore, provides the effective use for very high resolution satellite imagery with image interpreter to be used for many applications such as coastal area, urban and regional planning.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.