• Korean Journal of Remote Sensing
• /
• v.34 no.6_3
• /
• pp.1311-1317
• /
• 2018

The ultimate goal of satellite development is to use information obtained from satellites. Therefore, national-levelsatellite development program should include not only hardware development, but also infrastructure establishment and application technology development for information utilization. Until now, Korea has developed various satellites and has been very useful in weather and maritime surveillance as well as various disasters. In particular, KOMPSAT (Korea Multi-purpose Satellite) images have been used extensively in agriculture, forestry and marine fields based on high spatial resolution, and has been widely used in research related to precision mapping and change detection. This special issue aims to introduce a variety of recent studies conducted using KOMPSAT optical and SAR (Synthetic Aperture Radar) images and to disseminate related satellite image application technologies to the public sector.

• Korean Journal of Remote Sensing
• /
• v.18 no.3
• /
• pp.163-169
• /
• 2002

The land uses of Korean peninsula are very complicated and high-density. Therefore, the image classification using coarse resolution satellite images may not provide good results for the land cover classification. The purpose of this paper is to compare the classification accuracy of visual interpretation with that of digital image classification of satellite remote sensing data such as 20m SPOT and 30m TM. In this study, hybrid classification was used. Classification accuracy was assessed by comparing each classification result with reference data obtained from KOMPSAT-1 EOC imagery, air photos, and field surveys.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_4
• /
• pp.1935-1943
• /
• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.491-498
• /
• 2019

This study analyzed how the accuracy of co-registration varies depending on the convergence angles between two KOMPSAT-3·3A images. Most very-high-resolution satellite images provide initial coordinate information through metadata. Since the search area for performing image co-registration can be reduced by using the initial coordinate information, in this study, the mutual information method showing high matching reliability in the small search area is used. Initial coarse co-registration was performed by using multi-spectral images with relatively low resolution, and precise fine co-registration was conducted centering on the region of interest of the panchromatic image for more accurate co-registration performance. The experiment was conducted by 120 combination of 16 KOMPSAT-3·3A 1G images taken in Daejeon area. Experimental results show that a correlation coefficient between the convergence angles and fine co-registration errors was 0.59. In particular, we have shown the larger the convergence angle, the lower the accuracy of co-registration performance.

• Journal of Korean Society for Geospatial Information Science
• /
• v.16 no.3
• /
• pp.15-20
• /
• 2008

High-spatial resolution remote sensing satellites (IKONOS-2, QuickBird and KOMPSAT-2) have provided low-spatial resolution multispectral images and high-spatial resolution panchromatic images. Image fusion or Pan-sharpening is a very important in that it aims at using a satellite image with various applications such as visualization and feature extraction through combining images that have a different spectral and spatial resolution. Many image fusion algorithms are proposed, most methods could not preserve the spectral information of original multispectral image after image fusion. In order to solve this problem, modified induction technique which reduce the spectral distortion of fused image is developed. The spectral distortion is adjusted by the comparison between the spatially degraded pan-sharpened image and original multispectral image and our algorithm is evaluated by QuickBird satellite imagery. In the experiment, pan-sharpened image by various methods can reduce spectral distortion when our algorithm is applied to the fused images.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.1089-1100
• /
• 2018

In this paper, we analyze the feasibility of establishing a precise sensor model for high-resolution satellite imagery using unified control points. For this purpose, we integrated unified control points and the aerial orthoimages from the national land information map (http://map.ngii.go.kr/ms/map/NlipMap.do) operated by the National Geographic Information Institute (NGII). Then, we collected the image coordinates corresponding to the unified control point's location in the satellite image. The unified control points were used as observation data for establishing a precise sensor model. For the experiment, we compared the results of precise sensor modeling using GNSS survey data and those using unified control points. Our experimental results showed that it is possible to establish a precise sensor model with around 2 m accuracy when using unified control points.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_3
• /
• pp.1693-1705
• /
• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1283-1297
• /
• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.351-361
• /
• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Proceedings of the KSRS Conference
• /
• 2009.03a
• /
• pp.35-38
• /
• 2009

본 논문은 KOMPSAT-2, KOMPSAT-3 등과 같은 고해상도 위성영상의 정사보정 방법과 그에 따른 시험용 소프트웨어 개발을 목표로 한다. 정사보정은 위성 카메라의 자세나의 지표의 피복인위에 의하여 발생하는 인위를 제거하여 정사투영 된 특성을 갖는 영상을 구하는 과정을 말한다. 정사보정을 위해서는 위성 카메라의 기하학적인 특성과 지표면의 관계식을 나타내는 공선조건 식으로부터 지상기준점 및 수치표고모델을 통하여 구해진다. 본 논문에서는 고해상도 위성영상의 정사보정 방법을 구현하고, 실제 위성영상 데이터에 적용하여 구현된 소프트웨어의 성능을 평가한다.