• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• 항공우주기술
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• 제10권1호
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• pp.149-155
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• 2011

세계적 기후온난화와 이상기온현상으로 최근 급변하는 농업환경에 대응하기 위해서는 농작물 작황관리 및 예측시스템의 과학화를 통한 정부차원의 대처능력 개선이 시급하다. 농업분야에서 위성정보의 활용은 고해상도 광학 및 레이더 영상의 상용화와 더불어 정밀농업이라는 새로운 가능성을 열어주고 있다. 본 연구에서는 최근 농업분야에서 주목을 받고 있는 RapidEye 위성영상을 사용하여 우리나라 농촌지역의 토지이용유형별 분광식생지수의 시계열 특성을 살펴보았다. 식생과 비식생지역 간에 뚜렷한 시계열 변화양상이 나타났으며, 식생지역 내에서도 산림 수종별, 논 그룹별로 식생지수의 시계열 변화에 차이가 관찰되었다.

• 한국컴퓨터그래픽스학회논문지
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• 제25권3호
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• pp.21-29
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• 2019

본 논문에서는 구조-텍스처 분할 기법을 기반으로 위성영상을 분할 융합하여 공간 해상도를 개선시키는 프레임워크를 제시한다. 위성영상은 센서가 감지하는 파장에 따라 다양한 공간해상도를 가진다. 전정 영상 (panchromatic image)은 일반적으로 높은 공간해상도를 가지지만 단일 흑백컬러를 가지고 있는 반면, 다중분광 영상 (multi-spectral image)나 적외선 영상은 전정 영상에 비해 낮은 공간해상도를 가지지만 다양한 분광 밴드정보와 열 정보를 가지고 있다. 본 논문에서는 다중분광 영상이나 적외선 영상의 공간 해상도를 향상시키기 위해 영상의 디테일이 텍스처 영상에만 존재한다는 것에 착안하여 본 프레임워크를 고안하였다. 고안된 프레임워크에서는 저해상도 영상과 고해상도 영상이 구조 영상과 텍스처 영상으로 분할된 뒤, 저해상도 구조영상은 고해상도 구조 영상을 참조하여 가이디드 필터링 된다. 구조-텍스처 영상 모델에 따라 필터링된 저해상도 영상의 구조 영역과 고해상도 영상의 텍스처 영역을 픽셀 단위로 더해져서 최종 영상이 생성된다. 생성된 영상은 저해상도 영상의 밴드와 고해상도 영상의 디테일을 포함한다. 제시하는 방법은 분광해상도와 공간해상도를 모두 보존할 수 있음을 실험적으로 확인하였다.

• 대한원격탐사학회지
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• 제36권2_2호
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• pp.309-323
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• 2020

원격탐사를 이용한 연안 해역의 클로로필 농도 추정은 대부분 다분광 위성 영상 분석을 통해 수행되어 오고 있다. 최근에는 초분광 영상을 활용한 다양한 연구가 시도되고 있으며, 특히 항공기 기반 초분광 영상은 높은 공간 해상도로 좁은 밴드 폭을 가진 수백 개의 밴드로 구성되어 기존의 다분광 위성 영상을 통한 클로로필 추정보다 연안 해역에서 매우 효과적일 수 있다. 본 연구에서는 연안 해역의 클로로필 농도 추정을 위해 초분광 및 위성 기반 클로로필 영상을 비교 검증을 수행하였다. 한반도 남해안에서 수행된 현장조사로 획득된 클로로필 농도 자료와 해수 스펙트럼 자료를 분석한 결과, 높은 클로로필 농도를 갖는 해수 스펙트럼은 570 nm와 680 nm 파장대역 부근에서 peak를 보였다. 이러한 스펙트럼 특징을 활용하여 클로로필 농도 추정을 위한 새로운 밴드비(570 / 490 nm)가 제시되었고, 밴드비와 현장 클로로필 농도 간의 회귀 분석을 통해 새로운 클로로필 경험식이 생성되었다. 현장 클로로필 농도와의 검증 결과, R²의 0.70, RMSE와 mean bias가 각각 2.43와 3.46 mg m^-3으로 유효한 결과를 보였다. 새로운 경험식을 초분광 영상과 위성 영상에 적용한 결과, 초분광 클로로필 영상과 현장 클로로필 간의 평균 RMSE는 0.12 mg m^-3로 위성 클로로필 영상에서 보다 더 높은 정확도로 클로로필 농도 추정 가능하였다. 이 결과를 통하여 초분광 영상을 활용하여 보다 높은 정확도로 연안 해역 클로로필 농도의 고해상도 공간 분포 정보 제공이 가능할 것으로 기대된다.

• 한국측량학회지
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• 제34권4호
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• pp.413-423
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• 2016

Worldview-3 satellite sensor provides panchromatic image with high-spatial resolution and 8-band multispectral images. Therefore, an image-sharpening technique, which sharpens the spatial resolution of multispectral images by using high-spatial resolution panchromatic images, is essential for various applications of Worldview-3 images based on image interpretation and processing. The existing pansharpening algorithms tend to tradeoff between spectral distortion and spatial enhancement. In this study, we applied six pansharpening algorithms to Worldview-3 satellite imagery and assessed the quality of pansharpened images qualitatively and quantitatively. We also analyzed the effects of time lag for each multispectral band during the pansharpening process. Quantitative assessment of pansharpened images was performed by comparing ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), SAM (Spectral Angle Mapper), Q-index and sCC (spatial Correlation Coefficient) based on real data set. In experiment, quantitative results obtained by MRA (Multi-Resolution Analysis)-based algorithm were better than those by the CS (Component Substitution)-based algorithm. Nevertheless, qualitative quality of spectral information was similar to each other. In addition, images obtained by the CS-based algorithm and by division of two multispectral sensors were shaper in terms of spatial quality than those obtained by the other pansharpening algorithm. Therefore, there is a need to determine a pansharpening method for Worldview-3 images for application to remote sensing data, such as spectral and spatial information-based applications.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.504-507
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• 2007

The KOMPSAT-2 satellite is a push-broom system with MSC (Multi Spectral Camera) which contains a panchromatic band and four multi-spectral bands covering the spectral range from 450nm to 900nm. The PAN band is composed of six CCD array with 2528 pixels. And the MS band has one CCD array with 3792 pixels. Raw imagery generated from a push-broom sensor contains vertical streaks caused by variability in detector response, variability in lens falloff, pixel area, output amplifiers and especially electrical gain and offset. Relative radiometric calibration is necessary to account for the detector-to-detector non-uniformity in this raw imagery. Non-uniformity correction (NUC) is that the process of performing on-board relative correction of gain and offset for each pixel to improve data compressibility and to reduce banding and streaking from aggregation or re-sampling in the imagery. A relative gain and offset are calculated for each detector using scenes from uniform target area such as a large desert, forest, sea. In the NUC of KOMPSAT-2, The NUC table for each pixel are divided as HF NUC (high frequency NUC) and LF NUC (low frequency NUC) to apply to few restricted facts in the operating system ofKOMPSAT-2. This work presents the algorithm and process of NUC table generation and shows the imagery to compare with and without calibration.

• 대기
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• 제14권2호
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• pp.11-22
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• 2004

A technique on atmospheric correction algorithm to the multi-band reflectance of Landsat TM imagery has been developed using an atmospheric radiation transfer model for eliminating the atmospheric and surface diffusion effects. Despite the fact that the technique of satellite image processing has been continually developed, there is still a difference between the radiance value registered by satellite borne detector and the true value registered at the ground surface. Such difference is caused by atmospheric attenuations of radiance energy transfer process which is mostly associated with the presence of aerosol particles in atmospheric suspension and surface irradiance characteristics. The atmospheric reflectance depend on atmospheric optical depth and aerosol concentration, and closely related to geographical and environmental surface characteristics. Therefore, when the effects of surface diffuse and aerosol reflectance are eliminated from the satellite image, it is actually corrected from atmospheric optical conditions. The objective of this study is to develop an algorithm for making atmospheric correction in satellite image. The study is processed with the correction function which is developed for eliminating the effects of atmospheric path scattering and surface adjacent pixel spectral reflectance within an atmospheric radiation model. The diffused radiance of adjacent pixel in the image obtained from accounting the average reflectance in the $7{\times}7$ neighbourhood pixels and using the land cover classification. The atmospheric correction functions are provided by a radiation transfer model of LOWTRAN 7 based on the actual atmospheric soundings over the Korean atmospheric complexity. The model produce the upward radiances of satellite spectral image for a given surface reflectance and aerosol optical thickness.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.331-336
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• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the Korea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed and archived. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2004년도 GIS/RS 공동 춘계학술대회 논문집
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• pp.261-265
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat＋ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by Ministry of Construction & Transportation based on NGIS (National Geographic Information System) and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.