• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.449-461
• /
• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• Korean Journal of Remote Sensing
• /
• v.33 no.4
• /
• pp.339-350
• /
• 2017

In recent, cloud computing paradigm and open source as a huge trend in the Information Communication Technology (ICT) are widely applied, being closely interrelated to each other in the various applications. The integrated services by both technologies is generally regarded as one of a prospective web-based business models impacting the concerned industries. In spite of progressing those technologies, there are a few application cases in the geo-based application domains. The purpose of this study is to develop a cloud-based service system for satellite image processing based on the pure and full open source. On the OpenStack, cloud computing open source, virtual servers for system management by open source stack and image processing functionalities provided by OTB have been built or constructed. In this stage, practical image processing functions for KOMPSAT within this service system are thresholding segmentation, pan-sharpening with multi-resolution image sets, change detection with paired image sets. This is the first case in which a government-supporting space science institution provides cloud-based services for satellite image processing functionalities based on pure open source stack. It is expected that this implemented system can expand with further image processing algorithms using public and open data sets.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_4
• /
• pp.1327-1339
• /
• 2019

Surface reflectance obtained by absolute atmospheric correction from satellite images is useful for scientific land applications and analysis ready data (ARD). For Landsat and Sentinel-2 images, many types of radiometric processing methods have been developed, and these images are supported by most commercial and open-source software. However, in the case of KOMPSAT 3/3A images, there are currently no tools or open source resources for obtaining the reflectance at the top-of-atmosphere (TOA) and top-of-canopy (TOC). In this study, the atmospheric correction module of KOMPSAT 3/3A images is newly implemented to the optical calibration algorithm supported in the Orfeo ToolBox (OTB), a remote sensing open-source tool. This module contains the sensor model and spectral response data of KOMPSAT 3A. Aerosol measurement properties, such as AERONET data, can be used to generate TOC reflectance image. Using this module, an experiment was conducted, and the reflection products for TOA and TOC with and without AERONET data were obtained. This approach can be used for building the ARD database for surface reflection by absolute atmospheric correction derived from KOMPSAT 3/3A satellite images.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.408-408
• /
• 2023

하천공간은 하도, 사주, 식생, 하천구조물 등에 대한 특성을 지니고 있으며, 현장조사를 통해 하천공간에 대한 자료를 분석하여 기초자료를 생산한다. 기존에는 현장에서 육안조사나 지상에서 사진촬영, 스케치방법으로 하천공간특성에 대한 조사를 수행하였으나, 지상에서 조사한 자료은 하천특성에 대한 물리적·공간적 특성을 파악하기 어렵고 자료의 활용성이 낮은 한계점이 존재한다. 이와 같은 한계를 극복하기 위해 GIS 및 RS 기술을 활용한 고도화된 첨단조사 기술 및 장비가 도입되어 활용되고 있다. 본 연구에서는 하천공간특성을 GIS 기반으로 객체지향 분류 적용 연구와 분류 항목에 따른 공간분석 연구를 수행하였다. 연구를 위한 대상지역은 섬진강권역의 지석천 유역 하류부에 위치하고 있는 지석천 친수공원을 대상으로 선정하였다. 대상지역의 고해상도 항공영상을 수집 및 정합한 후 QGIS에서 제공하는 Orfeo ToolBox(OTB)의 LSMS(Large Scale Mean-Shift) 기법으로 정합한 항공영상의 객체지향 영상분할을 실시하여 벡터 레이어를 생성하였고, 하천공간특성에 따른 항목을 선정하여 각 항목의 영역에 대한 선별을 통해 훈련데이터를 생성하였다. 훈련데이터는 랜덤 포레스트를 이용하여 각 항목에 대한 자동 분류를 확인하였으며, 하천공간특성의 정량적 평가를 위해 분류된 각 항목별 공간분석을 통해 면적, 위치정보(위도, 경도, 표고)를 산정하였다. 분석 결과, 하천공간특성을 GIS 기반의 벡터 레이어와 각 항목에 대한 정량적 분석을 통해 하천공간의 DB를 구축하였다. 이와 같이 하천공간 DB 구축을 통해 전국 하천관리체계를 위한 기초자료를 구축하고자 하였다.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_2
• /
• pp.1509-1521
• /
• 2020

Experiments for validation of surface reflectance produced by Korea Multi-Purpose Satellite (KOMPSAT-3A) were conducted using Chinese Baotou (BTCN) data among four sites of the Radical Calibration Network (RadCalNet), a portal that provides spectrophotometric reflectance measurements. The atmosphere reflectance and surface reflectance products were generated using an extension program of an open-source Orfeo ToolBox (OTB), which was redesigned and implemented to extract those reflectance products in batches. Three image data sets of 2016, 2017, and 2018 were taken into account of the two sensor model variability, ver. 1.4 released in 2017 and ver. 1.5 in 2019, such as gain and offset applied to the absolute atmospheric correction. The results of applying these sensor model variables showed that the reflectance products by ver. 1.4 were relatively well-matched with RadCalNet BTCN data, compared to ones by ver. 1.5. On the other hand, the reflectance products obtained from the Landsat-8 by the USGS LaSRC algorithm and Sentinel-2B images using the SNAP Sen2Cor program were used to quantitatively verify the differences in those of KOMPSAT-3A. Based on the RadCalNet BTCN data, the differences between the surface reflectance of KOMPSAT-3A image were shown to be highly consistent with B band as -0.031 to 0.034, G band as -0.001 to 0.055, R band as -0.072 to 0.037, and NIR band as -0.060 to 0.022. The surface reflectance of KOMPSAT-3A also indicated the accuracy level for further applications, compared to those of Landsat-8 and Sentinel-2B images. The results of this study are meaningful in confirming the applicability of Analysis Ready Data (ARD) to the surface reflectance on high-resolution satellites.