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1 우주기술과응용 Journal of Space Technology and Applications pissn eissn 기술동향 SAR(Synthetic Aperture Radar) 영상 분석도구개발기술동향 이강진 1, 전성경 1, 성석용 1, 강기묵 2 1 ( 주 ) 솔탑 2 K-water 연구원 Technology Trend in Synthetic Aperture Radar (SAR) Imagery Analysis Tools Received: August 11, 2021 Revised: August 18, 2021 Accepted: August 18, 2021 Kangjin Lee 1, Seong-Gyeong Jeon 1, Seok-Yong Seong 1, Ki-mook Kang 2 1 SOLETOP, Co., Ltd., Daejeon 34051, Korea 2 K-water Research Institute, Water Resources Satellite Research Center, Daejeon 34045, Korea Corresponding author : Kangjin Lee Tel : Copyright 2021 The Korean Space Science Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ORCID Kangjin Lee Seong-Gyeong Jeon Seok-Yong Seong Ki-mook Kang 요약합성개구레이다 (SAR, synthetic aperture radar) 는주야간및기상조건에구애받지않고원하는지점을관측할수있다는장점으로인해최근그수요가점점늘어나고있다. SAR 위성으로관측한원시데이터는위성궤도보정, 방사보정, multi-looking, geocoding과같은전처리과정이필요하며, 사용자의목적에따라물체탐지, 변화탐지, DEM(Digital Elevation Model) 등영상활용을하기위해서는추가적인처리과정이요구된다. 이러한전처리와연산과정은매우복잡하며많은시간과컴퓨팅자원을필요로한다. 주로 SAR 영상을활용하는기관에서는영상을편리하고쉽게처리하기위해각기관의활용목적에맞는분석도구를개발하여사용및외부수요자들에게제공하고있다. 본논문에서는국내외에서이용하고있는대표적인 SAR 분석도구들의기능및특성에대해소개하고자한다. Abstract Recently, the synthetic aperture radar (SAR) has been increasingly in demand due to its advantage of being able to observe desired points regardless of time and weather. To utilize SAR data, first of all, many preprocessing such as satellite orbit correction, radiometric calibration, multi-looking, and geocoding are required. For analysis of SAR imagery such as object detection, change detection, and DEM(Digital Elevation Model), additional processings are needed. These pre-processing and additional processes are very complex and require a lot of time and computational resources. In order to handle the SAR images easily, the institutions that use SAR images develop analysis tools and provide users. This paper introduces the function and characteristics of representative SAR imagery analysis tools. 핵심어 : SAR(synthetic aperture radar) 영상분석, SAR 영상활용, SAR 영상분석도구 Keywords : SAR (synthetic aperture radar) imagery analysis, SAR imagery utilization, SAR imagery analysis tools 268

2 1. 서론 합성개구레이다 (synthetic aperture radar, SAR) 는능동 (active) 센서로위성또는비행체에서지상과해양으로마이크로파를순차적으로쏘아굴곡면에서반사되어돌아오는미세한시간차를처리하여지형도를만들거나지표를관측하는레이다시스템이다. 마이크로파주파수대를사용하기때문에수동 (passive) 방식인광학 (optical) 센서의가시 / 적외선파장대와는달리주야간및기상조건에영향을받지않아전천후관측이가능하다. SAR의개발은군용정찰장비로개발되기시작했으며, 점차민간분야로영역을넓혀가는중이다. SAR는관측하는위치에따라센서를비행물체에탑재하여대기중에서지표면을관측하는방식, 위성에탑재하여우주에서지구를관측하는방식, 지상에센서를설치하여관측하는방식으로구분된다. Table 1은 2010년이후발사된 SAR 위성의동향을보여준다. 특별 Table 1. List of SAR satellites launched since 2010 Satellite Country Launch Lifetime Wavelength Purpose RISAT-1 India years C Land observation KOMPSAT-5 South Korea years X Land observation ALOS-2 Japan years L Land observation Sentinel-1 Europe years C All-weather ocean and land high resolution multi-purpose observation SAOCOM Argentina years L Hydrology and land observation RCM Canada years C Multi-purpose SAR observation, expecially for ice NISAR USA / India 2023 planned 3 years L / S High-resolution all-weather imagery of ocean and land, especially suited for soil moisture BIOMASS Europe 2023 planned 5 years P To quantify the forest biomass, the extend of forest and deforested areas and the delimitation of flooded forests TanDEM-L Germany years L All-weather land observation, including surveillance and emergency management Capella USA years X All-weather high-resolution land observation; sea-ice ICEYE Finland years X All-weather high-resolution land observation; sea-ice SAR, synthetic aperture radar

3 SAR 영상분석도구개발기술동향 히주목할만한점은최근초소형 SAR 위성의개발이증가하고있다는것이다. 초소형 SAR 위성은대부분군집형태로운영이되는데, 초소형군집위성은상대적으로높은시공간해상도를갖고있어물체의식별이나모니터링에유용하다는장점이있다. 반면에촬영범위가상대적으로좁기때문에이동하는물체의경로추적이나넓은범위에서발생하는변화는분석하기어렵다는단점도있다 [1]. 현재개발중인대표적인초소형 SAR 위성으로는 ICEYE와 Capella 위성이있다. ICEYE는 18개의군집위성으로구성되며, 선박탐지를주목적으로한다 [1,2]. Capella 위성은 36개의군집위성으로구성되며, 1시간단위의짧은재방문시간을이용하여변화탐지를주목적으로한다 [2,3]. SAR 위성의경우, 해상도를향상시키기위해저고도의극궤도위성이대부분이며, 그에따라위성이이동하면서관측하고자하는목표지점을촬영하는방식을따르게된다. 위성의자세와관측모드등여러상황에따라관측되는영상의상태가제각각이기때문에일반적으로사용자에게익숙한 ground range와진북방향으로고정된정사영상으로변환해주는작업이필요하다. 또한지형을반영하기위한보정작업, 위상정보의적용유무등에따라결과영상은달라지게된다. 하지만위성궤도보정, 방사보정, multi-looking, geocoding과같은전처리작업은일반사용자들에게는굉장히어려운작업이고, 각단계별결과를해석하는방법도전문적인지식을토대로오랜경험과노하우가바탕이되어야한다. 이에따라전세계의 SAR 영상을활용하는기관에서는 SAR 영상을손쉽게처리하고분석할수있는분석도구를개발하여이용하고있다. 본논문에서는국내외대표적인 SAR 영상분석도구들의개발현황및활용범위등에대해살펴보고자한다. SAR 영상에대한수요가증가하고있지만, 현재까지국내에서개발하여사용가능한 SAR 위성은다목적실용위성 5호 ( 아리랑 5호 ) 가유일하다. 향후국내에서도차세대중형위성 5호 ( 수자원위성 ) 와같은중형급 (500 kg급 ) 위성뿐만아니라, 초소형위성에도 SAR 센서를탑재할것으로예상된다. 앞으로국내에도 SAR 영상의공급이더욱활발하게이루어짐에따라빠른시간안에보다효율적인영상활용을위해자체기술을통한영상분석도구의개발도병행되어야할것이다. 본논문을통해향후국내 SAR 위성영상분석도구를개발하는데있어여러대표적인 SAR 영상분석도구의주요기능및특징을파악하는데도움이되길기대한다. 2. SAR 분석도구의필요성 일반적으로 SAR 영상은위성으로부터관측한원시데이터 (raw data) 를지상국에서수신하고전처리과정을거쳐 SLC(Single Look Complex) 혹은 GRD(Ground Range Detected) 데이터와같은 Level 1 영상으로일반사용자에게제공된다. 사용자는제공된 Level 1 데이터를이용하여각자의활용목적에맞게추가적인처리과정을수행한다. SAR 영상을분석하고응용하는것에있어서가장많이쓰는것은 SAR 영상에서후방산란계수를도출하여그값을이용하는것이다. 후방산란계수값은물체의반사도에따라다른값을갖기때문에금속탐지, 선박탐지및분류등물체를탐지하는분야에서많이쓰인다. 뿐만아니라홍수피해탐지, 해양기름유출측정등재난 / 재해모니터링에도쓰이고있다. 후방산란계수를도출하는과정은각위성마다 SAR 영상을측정하는방법및관측모드등에따라조금씩다르지만기본적으로위성궤도보정, 방사보정, multi-looking, geocoding의 270

4 과정을거친다. 위성궤도보정 (satellite orbit correction) 은 SAR 영상을찍은위성의궤도정보를정확한값으로보정하여위성의위치에따른상대적인 SAR 데이터의오차를줄이는작업이다. 방사보정 (radiometric calibration) 은 SAR 영상의픽셀값이후방산란계수값을나타낼수있도록보정해주는작업이다. 기본적으로 Level 1 SAR 영상은방사보정이되어있지않아방사적편향이심한데, 방사보정은이러한편향을보정하고정확한후방산란계수값을구하기위해필수적인과정이다. 또한다른프로세스로처리된 SAR 영상과비교하려면보정후영상을비교해야정확한비교를할수있다. 단일로관측한 SAR 영상은 Speckle Noise가심하여그로부터산출한후방산란계수는오차가크다. 이를보정하기위하여 SAR 영상을다중으로사용하여평균을취해잡음을제거하는방식으로보정을하는데, 이를 multi-looking이라한다. Multi-looking의경우, 해상도는떨어지는효과가있지만필터링 (speckle filtering) 과달리후방산란계수의값이유지되는경우가많다. 반대로필터링의경우, 해상도는유지하지만후방산란계수의값의정확도가달라진다. SAR 영상분석에서는해상도보다후방산란계수의정확성을더요구하기때문에대부분 multi-looking의방식을선호한다. 지오코딩 (geocoding) 은변환된후방산란계수값을보정하기위하여지상의관측정보를활용하여후방산란계수값을업데이트하는식으로그값을보정하는작업이다. 이와같이 SAR 영상을활용하기위해서여러전처리과정을거친후에사용자가확인할수있는후방산란계수값이도출된다. 앞선내용과같이 SAR 영상의활용을위해서행해야하는전처리가상당히많고, 각과정에서도 SAR 영상의상태에따라세부적으로나뉘어서처리해야하는복잡함과어려움이있다. 뿐만아니라전처리과정은긴시간과많은자원을필요로한다. 이러한전문성을요구하는과정들은진입장벽이높기때문에 SAR 영상을활용하기원하는사용자측면에서 SAR 영상분석및활용에큰부담을가지게된다. 많은기관에서이런단점들을보완하고, SAR 영상처리과정을단순화하기위해분석도구를개발및배포하고있으며, 사용자친화적인 UI 형태로개발함으로써편리하게작업할수있도록하여 SAR 영상의활용을촉진하고있다. 3. 국내외 SAR 영상분석도구및활용현황 SAR 영상분석도구는다양한방식으로제공되는데, 일반적으로목적에맞는분석도구를개발하여설치프로그램을제공한다. 사용자는각자 PC에분석도구를설치한후 SAR 영상을수집하여분석하게되는데, 이는 PC 성능에따라처리속도가차이가난다는단점이있다. 또다른방법으로클라우드서비스를이용한플랫폼제공이있다. 클라우드서비스플랫폼은기본적으로사용자개인의 PC 환경에구애받지않고데이터수집절차도필요하지않다는장점이있으나, 여전히대용량의 SAR 영상을이용한많은전처리과정을거쳐야하는어려움이존재한다. 현재까지클라우드플랫폼은아직많이사용되고있진않지만, 각분야에서클라우드서비스가활성화됨에따라 SAR 분석도구또한클라우드플랫폼으로많이개발되고있다. 클라우드서비스를이용한대표적인분석도구로 HyP3(Hybrid Pluggable Processing Pipeline) 와 Capella가있다. 본장에서는국내외 SAR 영상처리및분석을위해개발된분석도구중주로이용되고있는 6개의분석도구에대해소개하고자한다. Table 2는각도구에대한일반적인특성을요약하여정리하였으며, 각각의도구에대한상세한설명은별도로기술한다

5 SAR 영상분석도구개발기술동향 Table 2. The characteristics of 6 SAR imagery analysis tools Name Company Country Utilization Access HyP3 ASF/DAAC USA Radiometric terrain correction Interferometric SAR Change detection A limited amount of processing for a user per month GAMMA GAMMA Switzerland DEM Displacement maps Land use maps Commercial SNAP S1TBX ESA Europe Calibration Speckle filtering Co-registration Orthorectification Mosaicking Data conversion Polarimetry Interferometry Free ENVI SARscape L3Harris USA Flood mapping Dem extraction Change detection Displacement map Ship detection Persistent scatters Image geocoding Commercial ISCE JPL, USA Earthquakes Stanford Univ. Volcanism Mountain building Micro-plate tectonics Members only PG- STEAMER Pixoneer South Korea Land cover map Disaster analysis Terrain information Application dependent; Free for non-profit SAR, synthetic aperture radar; ASF, Alaska Satellite Facility; DAAC, Distributed Active Archive Center; HyP3, Hybrid Pluggable Processing Pipeline; DEM, Digital Elevation Model; SNAP, SeNtinel's Application Platform; S1TBX, Sentinel-1 ToolBoX, ENVI, Environment for Visualizing Images; ISCE, InSAR Scientific Computing Environment; JPL, Jet Propulsion Laboratory; PG-STEAMER, Pixoneer Geomatics Software Tools for Exploitation And Management of Earth Resources. 3.1 ENVI (Environment for Visualizing Images) ENVI(Environment for Visualizing Images) 는 L3Harris에서자료처리및분석플랫폼으로위성영상을처리하고분석하는목적으로개발하였다. ENVI 는각종기본기능및 add-on 272

6 모듈을통해기타영상및레이다, 라이다등의데이터처리도지원하며, 사용자가최선의결정을내리는데필요한정확한정보를제공한다. 이미지분석가, GIS 전문가및과학자가지리공간이미지에서신뢰할수있는정확한정보를추출하는데사용된다. 멀티스펙트럼, 하이퍼스펙트럼, 열, LiDAR 및 SAR 를포함한모든유형의데이터를지원하며, 별도의프로그래밍이필요없이직관적인도구와워크플로우를통해사용자들이딥러닝에액세스할수있다는장점이있다. ENVI의 SARscape는 SAR 영상분석을위한 SARscape analytics toolbox 모듈을지원한다. SARscape analytics toolbox 는홍수탐지, 선박탐지, 변화탐지등 SAR 영상을활용한 12 가지응용및분석법을하나의 GUI로처리할수있도록만들어진모듈이다 (Fig. 1). 일반적인 SAR 영상처리는많은세부적조정과처리순서가중요하지만, 이모듈을통하여사용자는복잡한처리과정을직접구성하지않고, 손쉽게목적에맞는처리과정을진행할수있다. 또한몇가지옵션을변경하여추가적인목표에따른처리를수행할수있다 [4]. ENVI는 Windows 또는 Linux 운영체제기반이며, 최소사양으로 8 GB 이상의 RAM과 4코어이상의 CPU를권장한다. 3.2 GAMMA GAMMA가개발하고관리하는상용소프트웨어로 SAR 데이터에서 DEM(Digital Elevation Model), 변위맵및토지이용맵과같은최종산출물을산출할수있는전체처리프로세스를지원한다. 특별히 Fig. 2와같이 SAR 영상의 interferometric 프로세싱목적으로개발되어여러모듈이포함되어있으며, 각모듈은구조화된코드로구성되어있다. 프로그램은커맨드라인상에서개별적으로실행될수있으며스크립트를통해자동화하여효율적으로처리할수도있다. GAMMA 에서적용가능한위성데이터로는 AR ERS-1/2, JERS-1, SIR-C/X-SAR, RADARSAT-1/2, ENVISAT ASAR, ALOS PALSAR, TerraSAR-X, Cosmo-SkyMed 등이있으며, 위성영상을활용하기위한주요기능은 Table 3과같다 [5,6]. GAMMA는 UNIX 운영체제기반이며, PC/LINUX 및 PC/NT와함께워크스테이션에서실행가능하다. Fig. 1. Analysis of SAR imagery observed by Sentinel-1. The ENVI SARscapce allows users to easily display, monitor and automatically extract information from SAR data. SAR, synthetic aperture radar; ENVI, environment for visualizing images

7 SAR 영상분석도구개발기술동향 Fig. 2. Results of SAR processing using GAMMA. Sentinel-1 interferogram over oil fields in Yibal, Oman (left) and SAOCOM-1A Pauli decomposition of TOPS(Terrain Observation with Progressive Scans) acquisition over Formosa province, Argentina (right). SAR, synthetic aperture radar. Table 3. The key features of GAMMA Function Description Modular SAR Processor Range compression with optional azimuth prefiltering (MSP) Autofocus Azimuth compression Multi-look post processing Interferometric SAR Precision registration of interferometric image pairs Processor (ISP) Adaptive filtering of interferograms Phase unwrapping Generation of topographic height Differential Interferometry Differential interferometric processing and Geocoding Software Required for 2-pass differential interferometry (DIFF & GEO) Interferometric Point Surface deformation histories, terrain heights, and relative Target Analysis (IPTA) atmospheric path delays Optimal match to the observed interferometric phases Land Application Tools Filtering (LAT) Classification Visualization Mosaicking Geocoding and Image Generate multi-look intensity image (MLI) in slant range geometry Registration Software Co-register multiple images in slant range geometry (GEO) Display and raster file generation tools Display Tools (DISP) Single look complex and detected multi-look intensity SAR images Interferograms, unwrapped phase, and interferometric correlation Display and editing of phase unwrapping flag files SAR, synthetic aperture radar

8 3.3 HyP3 (Hybrid Pluggable Processing Pipeline) HyP3(Hybrid Pluggable Processing Pipeline)는 ASF(Alaska Satellite Facility) DAAC (Distributed Active Archive Center)에서 만든 오픈소스 툴로 사용자 맞춤형 SAR 프로세싱 을 제공한다. HyP3는 최대 월 단위 처리 서비스를 제공하며, 현재 베타 상태로 접근이 제한 되어 있다. HyP3는 ASF의 데이터 검색 도구인 Vertex에 통합되어 있어 사용자가 지구상의 관심 영역을 쉽게 선택할 수 있으며, 사용 가능한 SAR 영상을 찾아 방사선 측정 지형 보정 (RTC), 간섭계 SAR(InSAR), 변화 탐지 처리를 수행한다(Fig. 3). 빠른 결과 생성을 위해 각각 의 처리는 AWS 클라우드 컴퓨팅을 활용하여 병렬로 수행되며, 처리과정에서 사용자가 최종 산출물을 정의할 수 있는 옵션을 제공한다. 또한 Vertex UI 외에도 프로그램 액세스와 HyP3 를 사용자 워크플로우에 구축할 수 있는 RESTful API 및 Python 소프트웨어 개발자 키트 (SDK)를 제공한다. HyP3는 SAR 처리에 대한 많은 지식을 가지고 있지 않아도 사용자가 이 용함에 있어서 입력 데이터와 옵션만 설정하면 쉽게 처리된다는 장점이 있다. 일본 PALSAR RTC의 co-registered 세트와 같은 UTM 좌표계로 투영된 co-registrated 이미지셋은 모두 분석 가능하며, ASF DAAC에 의해 보관 및 배포된다[7,8]. 3.4 ISCE (InSAR Scientific Computing Environment) ISCE(InSAR Scientific Computing Environment)는 객체 지향 언어를 기반으로 NASA의 JPL(Jet Propulsion Laboratory)과 스탠포드 대학교가 협업하여 개발하였다. 사용자의 편의성, 효율성, 유연성, 확장성을 고려하여 SAR Processing 환경을 구축하였으며, 입력 데이터 형식 은 GDAL(The Geospatial Data Abstraction Library) format뿐 아니라 TIFF, GeoTIFF, ENVI, Fig. 3. On demand processing in Vertex which is ASF s data search portal. Vertex is the easiest way to use Hyp3 and provides a friendly interface. ASF, Alaska Satellite Facility; HyP3, Hybrid Pluggable Processing Pipeline

9 SAR 영상분석도구개발기술동향 NetCDF, HDF5 등을지원한다. 프레임워크는파이썬기반이며, 소스코드는처리엔진을통해입출력동작을분리하여구성된다. 이를통해입력데이터에대한유연성과확장성이향상되며, 새로운데이터형식및센서를사용할때소스의수정이필요한상황에서프로그램에미치는영향이적다는장점이있다. ISCE 에서는 ALOS, ERS, ENVISAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2 및 TerraSAR-X 등의위성데이터를처리할수있으며, Fig. 4와같이주로 InSAR 분석을수행한다. ISCE는최근 Sentinel-1A SAR 데이터 (stripmap 및 TOPS(Terrain Observation with Progressive Scans) 모드 ) 를처리하기위한구성요소와 TOPS 모드데이터를처리하기위한새로운워크플로우가개발되어추가되었다. ISCE 는리눅스, 유닉스, MacOS 등다양한운영체제를기반으로동작한다 [9]. 3.5 PG-STEAMER(Pixoneer Geomatics Software Tools for Exploitation And Management of Earth Resources) PG STEAMER(Pixoneer Geomatics Software Tools for Exploitation And Management of Earth Resources) 는국내기업인픽소니어에서개발한원격탐사자료에대한처리, 분석, 가공및출력을위한전문적이고통합적인소프트웨어솔루션으로별도의모듈이없는하나의통합패키지로구성되어있다. 다양한종류의위성영상자료, 벡터자료를읽어들일수있 Fig. 4. Visualizing the output products of an UAVSAR interferogram on August 24, The strong fringe caused by the South Napa earthquake is shown in ISCE. ISCE, InSAR Scientific Computing Environment. UAVSAR, Uninhabited Aerial Vehicle Synthetic Aperture Radar

10 으며, 주요 기능으로 위성영상으로부터 정보를 추출하기 위한 기하보정, 정사보정, 모자이크, 분류 등의 각종 영상처리 및 분석기능, DEM 생성, 3차원 영상분석, 위성영상 지도 제작, GIS 기능 등을 제공한다. 테라바이트(TB) 이상의 대용량 위성영상 데이터를 실시간으로 육안 분 석할 수 있고, 또한 PG-STEAMER 4.0 버전부터 실시간 프로세싱(RTP, real time processing) 이 가능하다. Fig. 5에서 볼 수 있듯이 아이콘 기반의 사용자 인터페이스로 사용자가 메뉴를 선택하는 데 있어 매우 직관적이며, 또한 RTP 기법을 적용하여 초보자도 쉽게 사용을 할 수 있도록 구현되었다. PG-STEAMER의 주요 상세 기능은 Table 4와 같다[10]. Fig. 5. Image processing of a sample SAR imagery observed by KOMPSAT-5. PG-STEAMER has robust image processing tools that are easy to use interface and functionality. SAR, synthetic aperture radar. PG-STEAMER, Pixoneer Geomatics Software Tools for Exploitation And Management of Earth Resources. Table 4. The key features of PG-STEAMER Function Catalog Description View metadata from raster (xdm, jpeg, bmp, tiff, MrSID, JPEG2000) and Vector (xvc, dxf, shp, dgn, xyz) Preview data file (raster, vector) Export to HTML file from metadata Image Composer/Analyzer Open multiple datasets in multiple coordinate system Auto-warping and mosaic with raster and vector bands Statistics (histogram, scattergram) Vector file overlay Vector Composer Support DXF, DGN, SHP, and XVC file Coordinate conversion from vector file Interactive histogram stretch 277

11 SAR 영상분석도구개발기술동향 Table 4. Continued Function Description 3D Composer Map Composer Tools & Utilities Image Processing DEM Tools Interactive 3D view Path analysis Line of sight analysis Auto gridding for map image Vector overlay on map image Tile printing for big workspace Mosaic images Convert map projection Coordinate conversion Image local warping Geometric correction Auto GCP(ground control point) matching Orthorectification GCPs & RPC Spatial filtering Stereo DEM generation/upgrade DEM generation from vector generation DEM from XYZ DEM interpolation 3.6 SNAP S1TBX (SeNtinel's Application Platform Sentinel-1 ToolBoX) SNAP(SeNtinel's Application Platform) 은 ESA가개발한공개소프트웨어로서 SAR와광학위성을포함한 Sentinel 위성시리즈에서얻은자료를처리하는여러개의 Toolbox로이루어져있다. S1TBX(Sentinel-1 ToolBoX) 는주로 Sentinel-1A/B 영상과간섭기법을처리하기위한프로그램으로대중적으로쓰이고있는 SAR 분석도구중하나이다. SAR 영상처리에필요한 calibration, speckle filtering, coregistration, orthorectification, mosaicking, data conversion, polarimetry, interferometry 등대부분의처리과정을진행할수있으며, 자체적으로그래프, 통계, 도수분포와같은분석과지도 / 비지도분류도제공하고있다. Fig. 6 은 Sentinel-1 위성영상을이용한전처리프로세싱의모습을보여준다. 한편, SNAP은단순한 SAR 영상처리외에도 Graph Builder와같은흐름도방식의자료처리기법을제공한다. Graph Builder는사용자의목적에맞도록기본처리과정을구성하고, 사용자의요구에따라별도의처리또한손쉽게추가하여분석할수있다. 또한 DEM 자동다운로드및모자이크등을포함한편리한기능을탑재했다. 그밖에도 SNAP을통해처리한 SAR 영상을타분석도구나위성자료의파일형식으로변경하여내보낼수있다. 이처럼 SNAP은타위성자료를처리할뿐아니라, 타분석도구의전처리과정역할도수행할수있다. SNAP은 Windows, Unix, MacOS 등다양한운영체제를기반으로동작하며, 최소사양으로 8 GB 이상의 RAM 과 4코어이상의 CPU를권장한다 [11]

12 Fig. 6. Analysis of Sentinel-1 SAR imagery using SNAP. SAR, synthetic aperture radar; SNAP, SeNtinel s Application Platform. 4. 결론및제언 지금까지 SAR 영상분석도구는기본적인 SAR 처리및시각화에서부터간섭계및시계열분석에이르기까지각각의목적에맞게다양하게개발이되어오고있다. 하지만 SAR 영상분석도구는기본적으로진입장벽이높기때문에많은사용자들이어려움을겪고있는실정이다. 이러한문제점을최대한극복하기위해향후개발되는도구들은목적에맞게정의하고패키지화하여제공이되어야할것이다. 또한사용자가이해하기쉽고다루기용이하도록 UI를구성하는것도매우중요하다. 한편, SAR 영상은기본적으로용량이크기때문에원활한분석및활용을위해서는저장용량, 메모리, GPU 등하드웨어성능도충분히뒷받침되어야할것이다. 2022년부터본격적으로개발될예정인차세대중형위성 5호는세계최초중형급 C-밴드 SAR 위성으로수자원 / 수재해활용산출물생성을통해수자원분야위성활용미래기술을선도할것으로기대된다. 차세대중형위성 5호의주된목적은 1) 홍수, 토양수분, 가뭄등수량중심의수자원변동모니터링, 2) 수자원시설물안정성분석및시계열감시, 3) 레이다를활용한환경 / 생태분야모니터링이다. 본논문에서소개한주요 SAR 영상분석도구들의구조및기능을참고하고장점들을모아수자원 / 수재해분야맞춤형분석도구를개발한다면국내뿐아니라전세계적으로 SAR 위성영상을활용하는데많은기여를할것으로기대된다. 감사의글 본연구는환경부한국환경산업기술원의수요대응형물공급서비스연구사업 ( ) 에의해수행되었습니다. References 1. Kim Y, Kang K, A study on the utilization of SAR microsatellite constellation for ship detection, Korean J. Remote Sens. 37, (2021)

13 SAR 영상분석도구개발기술동향 2. Paek S, Balasubramanian S, Kim S, Weck O, Small-satellite synthetic aperture radar for continuous global biospheric monitoring: a review, remote Sens. 12, (2020) Capella Space, Visualizing a changing world (2021) [Internet], viewed 2021 Aug 17, available from: 4. l3harristm, ENVI SARscape products (2021) [Internet], viewed 2021 Jul 20, available from: 5. GAMMA Remote Sensing, The GAMMA Software (2021) [Internet], viewed 2021 Jul 20, available from: 6. GAMMA Remote Sensing, GAMMA Software (2021) [Internet], viewed 2021 Jul 20, available from: 7. Alaska Satellite Facility [ASF], ASF Hyp3 (2021) [Internet], viewed 2021 Jul 20, available from: 8. Hogenson K, Arko SA, Buechler B, Hogenson R, Herrmann J, et al., Hybrid pluggable processing pipeline (HyP3): a cloud-based infrastructure for generic processing of SAR data, Proceedings of the 2016 AGU Fall Meeting, San Francisco, CA, Dec Rosen PA, Gurrola E, Sacco GF, Zebker H, The InSAR scientific computing environment, Proceedings of the EUSAR 2012; 9th European Conference on Synthetic Aperture Radar, Nuremberg, Apr 2012, Pixoneer, PG-STEAMER (n.d.) [Internet], viewed 2021 Jul 20, available from: pixoneer.co.kr/products/pg-steamer/pg-introduce/ 11. European Space Agency [Esa] Tutorials, [Internet], viewed 2021 Jul 20, available from:

14 Author Information 이강진 경희대학교우주탐사학과에서우주기상으로박사학위를취득하였으며, 2016년부터 2019년까지한국전자통신연구원에재직하며후속정지궤도기상위성지상국개발사업에참여하여우주기상자료처리시스템개발을담당하였다. 현재는 ( 주 ) 솔탑에서수자원 SAR 위성, 후속해양탑재체개발, 차세대기상기후관측위성등에관련된연구, 기획및관리업무를수행하고있다. 성석용 공주대학교대기과학과이학사를취득하고, 대기환경모델링연구실에서석사과정진행중이며현재수료상태이다. 2016년부터 ( 주 ) 솔탑에입사하여 2019년까지후속정지궤도기상위성 ( 천리안 2A호 ) 지상국개발사업의기상알고리즘모듈화 / 표준화및테스트베드서버운영 / 관리업무를수행했다. 이후현재까지천리안 2A호위성지상국시스템유지보수, 공군기상단극 / 저궤도위성을이용한구름연직분포연구, 수자원 SAR 위성, 후속해양탑재체개발등의업무를수행중이다. 전성경 경희대학교우주탐사학과에서우주기상으로석사학위를취득하고, 2018년부터동대학에서박사과정을진행중이다. 현재는우주기상분야에딥러닝을적용하는연구를진행중이다. 2021년부터 ( 주 ) 솔탑에입사하여 AI 기반위성영상활용연구기획및수자원 SAR 위성에관련된업무를수행중이다. 강기묵 서울대학교지구환경과학부에서인공위성지구물리전공으로박사학위를취득하였으며, 2018 년부터 2019년까지서울대학교기초과학연구소에재직하며, SAR 위성영상활용기술연구를수행하였다. 2019년부터 K-water 연구원에서수자원위성 ( 차세대중형위성 5호 ) 탑재체핵심기반기술및재난상황시위성영상활용기술등에관련된연구를수행하고있다

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