• Korean Journal of Remote Sensing
• /
• v.33 no.6_3
• /
• pp.1179-1185
• /
• 2017

The KOMPSAT-3A satellite acquires high-resolution MWIR images twice a day compared to conventional Earth observing satellites. New radiometric information of Earth's surface can be provided due to different characteristics from existing SWIR images or TIR images. In this study, the difference image of multitemporal images was generated and compared with existing infrared images to find the characteristics of KOMPSAT-3A MWIR satellite images. A co-registration was performed and the difference between pixel values was minimized by using PIFs (Pseudo Invariant Features) pixel-based relative normalization. The experiment using Sentinel-2 SWIR image, Landsat 8 TIR image, and KOMPSAT-3A MWIR image showed that the distinction between artifacts in the difference image of KOMPSAT-3A is prominent. It is believed that the utilization of KOMPSAT-3A MWIR images can be improved by using the characteristics of IR image.

• Korean Journal of Remote Sensing
• /
• v.2 no.2
• /
• pp.107-116
• /
• 1986

A unique approach to access Landsat satellite imagery has been implemented on IBM PC microcomputer in order to generate image maps to be used as a substitute and/or supplement for a conventional topographic map. This method enables user to automatically: o extract a nominal image map, o geoencode or calibrate as an image map, and o create a multitemporal image file using CCTs containing precision processed Landsat MSS and TM images. These map extraction process includes: o location of map area in the selected CCT, o conversion of map coordinates to image coordinates, o extraction of map area, and o rotation of image to the true North/South and East/Weat direction.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_4
• /
• pp.1085-1095
• /
• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.46 no.7
• /
• pp.15-21
• /
• 2004

This study is to present a standardized agricultural land use classification scheme at various spatial resolution (from 1 m to 30 m) of satellite images including Landsat TM, KOMPSAT-1 EOC, ASTER VNIR and IKONOS panchromatic (PAN) and multi-spectral (MS) images. The satellite images were interpreted especially for identifying agricultural land use, crop types, agricultural facilities and structures of 18 items. It was found that there is a threshold spatial resolution between 4 m and 6.6 m to identify the full items. Thus it is suggested that IKONOS fusion image (MS enhanced by PAN) is required to produce land use map for agricultural purpose.

• Journal of Korean Society for Geospatial Information Science
• /
• v.18 no.4
• /
• pp.119-129
• /
• 2010

The joint use of remotely sensed data and field measurements has been widely used to estimate aboveground carbon stock in many countries. Recently, Korea Forest Research Institute has developed new carbon emission factors for kind of tree, thus more accurate estimate is possible. In this study, the aboveground carbon stock of Danyang area in South Korea was estimated using k-Nearest Neighbor(kNN) algorithm with the 5th National Forest Inventory(NFI) data. Considering the spectral response of forested area under the climate condition in Korea peninsular which has 4 distinct seasons, Landsat TM seasonal satellite images were collected. As a result, the estimated total carbon stock of Danyang area was ranged from 3542768.49tonC to 3329037.51tonC but seasonal trends were not found.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.249-255
• /
• 1999

Water pollution is becoming a serious problem in the populous cities and coastal areas near industrial complex. Sometimes, phytoplankton is considered as the most important element in the coastal environment. Phytoplankton is easily estimated by measuring chlorophyll content in the laboratory. In this study, to build up estimating algorithm of the chlorophyll amount related to the monitoring of coastal environments in Kwangyang bay, the correlationship the respective in situ observed data with Landsat TM and SeaWiFS satellite Image was analyzed. It showed that Landsat TM band 3 image has the highest correlationship with observed data, and based upon this result the monitoring algorithm of chlorophyll in coastal area was extracted. This algorithm will be an important for extracting and controlling environment elements in coastal areas in the future. And it has a significant meaning that it has established a spatial data construction in which satellite image alone could monitor the coastal environment.

• Korean Journal of Remote Sensing
• /
• v.29 no.5
• /
• pp.443-459
• /
• 2013

Located on Java subduction zone, Merapi volcano is an active stratovolcano with a volcanic activity cycle of 1-5 years. Merapi's eruptions were relatively small with VEI 1-3. However, the most recent eruption occurred in 2010 was quite violent with VEI 4 and 386 people were killed. In this study, we have attempted to study the characteristics of Merapi's eruptions during 18 years using optical Landsat images. We have collected a total of 55 Landsat images acquired from July 6, 1994 to September 1, 2012 to identify pyroclastic flows and their temporal changes from false color images. To extract areal extents of pyroclastic flows, we have performed supervised classification after atmospheric correction by using COST model. As a result, the extracted dimensions of pyroclastic flows are nearly identical to the CVP monthly reports. We have converted the thermal band of Landsat TM and ETM+ to the surface temperature using NASA empirical formula and calculated time-series of the mean surface temperature in the area of peak temperature surrounding the crater. The mean surface temperature around the crater repeatedly showed the tendency to rapidly rise before eruptions and cool down after eruptions. Although Landsat satellite images had some limitations due to weather conditions, these images were useful tool to observe the precursor changes in surface temperature before eruptions and map the pyroclastic flow deposits after eruptions at Merapi volcano.

• Korean Journal of Remote Sensing
• /
• v.38 no.1
• /
• pp.103-110
• /
• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Korean Journal of Remote Sensing
• /
• v.28 no.6
• /
• pp.683-691
• /
• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• Spatial Information Research
• /
• v.11 no.1
• /
• pp.13-22
• /
• 2003

The study of geomorphology of tidal flat using remote sensing image has been considered useful because of it's ability to acquire data periodically. Especially, the Near Infrared band of satellite image has been used to divide between land and sea area. This study extracted a borderline of the tidal flat using Landsat-5 images and generated DEM(Digital elevation model) using tide level data as elevation value. DEM is a useful tool for three-dimensional survey of geomorphology and can be used for survey of tidal flat. This study divided 8 images of 1990's into two parts - before 1994 and after 1994 - and generated DEM respectively. In this work, the areas of tidal flats are calculated and it was revealed the area of tidal flat was decreased after 1994.