multidatabase systems and interoperability issues

소형 위성군 연구 동향 및 연구결과 소개

2017. 12. 01

한상혁 Ph.D/ITPE

Korea Aerospace Research Institute

2017년국내초소형위성개발자워크샵

목차TABLE OF CONTENTS

위성군사례및연구동향

관련연구결과

Ⅰ.위성군 사례 및 연구동향

1. 위성군 사례 및 연구동향

❖ 초소형 위성군 서비스 분류

Earth Observation : Visible IR, SAR Traffic Monitoring Service : AIS ADB-S

Weather and Environment Monitoring

Service

Communication : Relay

Satellite

❖ 초소형 위성군

Aerial & Maritime : Monitoring aircraft(ADB-S) and Vessels(AIS)($ 7.2M)

AIS Tech : Constellation of 25 Sat / Thermal Imaging and Aviation Tracking

Analytical Space : In-Orbit relay communication and laser communication

Astro Digital : Earth Observation with 6U and 12U($ 16.7M+)

ASTROCAST : 64EA 3U Constellation($ 5.85M)

Blink Astro : Small Ground transmitter and cloud based data analytics service

Blue Field : Methane Tracking with 16U

Capella Space : SAR Constellation($ 12+M)

Douria AeroSpace : Earth Observation with 6U and 16U

Deep Space Industries : Asteroid Prospecting 6U

Dunvegan Space Systems : 24EA BitSat / Communication

EarthCube : Infrared Imaging constellation

Fleet Space : 12U CubeSat



GeoOptics : Using GPS Occultation for weather Data($ 5.15M)

GHSat : Greenhouse gas and air quality gas emissions monitoring

Harris Corporation : 12EA 6U for Weather wind data

HawkEye 360 : Communication

Helios Wire : 16U Cubesat

HyperCubes : Pollution tracking

Iceye : SAR Constellation

ISIS : AIS Data service

Karten Space : Earth Observation and AIS Data service with 6U

Koolock : Earth Observation with IR

Magnitude Space :

NSLCOmm : Communication network with 60 EA with 1Gbits 3U

OQ Technology

Orbital Micro Systems : weather constellation

Planet : Earth Observation($ 183M)



Planetary Resource : Earth Observation with midwave infrared($ 50+M)

PlanetiQ : Weather data based on GPS radio occulation($ 5+M)

Promethean Labs : Measuring Greenhouse gas

Rangnarok Industries : Polar Broadband Service

Reaktor Space : Hyperspectral Image Constellation

Sky and Space Global : Communication Service($ 11.5M)

SpacePharma : Microgravity Service

Spire : Weather Monitoring, AIS, ADB-S($ 69.5M)

Transcelestial Technology : Laser Network

Tyvak

406 Aerospace : Weather Data

4Skies

Macro AeroSpace : Space Data Storage

Outernet : Broadcast service

Xoterra AeroSpace : 88EA CubeSats for Earth Observation


❖ Planet Labs

서비스 내용

➢ Planet Monitoring

➢ Hi Res Monitoring

➢ Open Water Monitoring

➢ Planet Basemaps

➢ Planet Imagery

➢ Planet Archive

➢ Platform

➢ Planet Explorer Beta

➢ Open California

➢ Application Developer Program

➢ Education and Research Program


❖ Planet Labs

위성군 구성 및 위성 스펙


❖ Spire

서비스 내용

➢ Maritime

➢ Weather

➢ Aviation

➢ Custom


➢ Lemur-1, Lemur-2

➢ 125 satellites

➢ Automatic Identification System

➢ weather payloads measure temperature, pressure and precipitation.


❖ Planetary Resource

서비스 내용

➢ Planetary Resource

➢ Earth Observation with IR


➢ Arkyd-6

➢ mid-wave infrared sensor

➢ Second-generation System

➢ broadband imager spanning 3 to 5 microns within the infrared region of the electromagnetic spectrum.

▪ This region is sensitive to the presence of water

▪ hydrated minerals

▪ thermal energy


❖ Astro Digital

서비스 내용➢ 6U and 16U CubeSat➢ fresh images of the planet every

day➢ AGRICULTURE, DISASTER

MANAGEMENT, FOREST MANAGEMENTURBAN DEVELOPMENT, BUSINESS INTELLIGENCE

위성군 구성 및 위성 스펙➢ Landmapper-HD, 20EA

▪ Imaging the world at 2.5 meters resolution, the spacecraft weighs 20kg and is about the size of a small microwave

➢ Landmapper-BC▪ 22 meters resolution, the

spacecraft weighs 10kg and is about the size of a shoebox.

▪ 1TB of imagery.


❖ Sky and Space Global

서비스 내용➢ Band width satellite

communication providers➢ Machine-to-Machine➢ Real-time tracking for airliners

and shipping companies➢ Complementary service for

cellular networks in Latin Americas, Asian and African regions with poor coverage

➢ Ad-hoc disaster and crisis areas solutions

➢ Premium secure services

위성군 구성 및 위성 스펙➢ Goal : 200 EA➢ 4-way inter-satellites links➢ Onboard orbit control➢ Autonomous optimal network

management➢ Fully independent space segment


❖ Capella Space

서비스 내용

➢ Maritime Domain Awareness

➢ City Planning and Mapping

➢ Oil and Energy

➢ Patterns of Life

➢ Business Intelligence

➢ Agriculture


➢ SAR

➢ Goal : 40 EA

➢ 2017년 Q4 발사 예정


❖ ICEYE

서비스 내용

➢ Maritime Domain Awareness

➢ City Planning and Mapping

➢ Oil and Energy

➢ Patterns of Life

➢ Business Intelligence

➢ Agriculture


➢ SAR


Ⅱ.연구결과

과제명 : “중소기업상용화지원사업”

Techniques for Nanosatellite Constellation

Space Segment

• Payload

– High Resolution Camera

– Radar or None Visible Payload

• BUS

– Inter-Satellite Communication

– Relative Orbit control and Determination System

– Mass Data Processing On Board Computer

– High Frequency Transceiver

Ground Segment

• Big Data Processing & Analytic Technique

• Contact Schedule Managing

위성간 데이터 동기화 기술

빅데이터, 딥러닝 기술

2. 연구결과 위성간데이터동기화기술

❖ Research Objective

Development of data synchronization module for small satellites constellation

❖ Research SignificanceDevelopment of technique for small satellites constellation mission

Various applications

➢ Ex) Drone constellation / landing, vehicle-to-vehicle communication


❖ Research Reference

Data synchronization module for small satellite constellation is developed based on patent “APPARATUS AND METHOD FOR DATA SYNCHRONIZATION IN MULTIPLE SATELLITES” (patent number 10-16202330000)

(Han, 2013)


❖ Data Synchronization Module Overview Data synchronization process is as follows

➢ Determine synchronization target

➢ Determine synchronization data

➢ Form synchronization log

➢ Send and receive synchronization data


❖ Software Simulation

Scenario : Sync position and velocity data

→ Check whether TX satellite sends its sync data according to its configuration file

→ Check RX satellite receives sync data

RX

Satellite

Onboard

Propagator

Data Sync.

통신

Socket

통신

TX

Satellite

Onboard

Propagator

Data Sync.

Socket

통신


❖ Software Simulation

Performed hardware simulation with two Raspberry Pis

→ Socket is used for internal communication

→ VNC Viewer is used for control of Raspberry Pis


❖ Software Verification

TX satellite

RX satellite


Satellite

Image

GDAL

TranslateSciDB

GDAL

TranslateVisualize

GDAL

read

Satellite

Image

Tiff to Scidb Scidb to Tiff

2001-04-01, NDVI, H12V9 2004-04-01, NDVI, H12V9

Dev. Language : R

2. 연구결과 빅데이터처리기술

Research Steps

1. Study Bigdata processing techniques for Satellite Imagery

• Reference [1]

• => it’s multi-dimensional array database technology; SciDB

2. Study image processing techniques based SciDB

• Reference [2-4]

• Image processing technique : GDAL

• Image processing with SciDB : GDAL4SciDB

3. Visualize of MODIS satellite data by SciDB & GDAL

• Reference [2-3]

MODIS; Moderate Resolution Imaging Spectroradiometer, NASA Terra

Swath 2,330 km, every 1-2 day in the world, 1 km spatial resolution


Bigdata processing technique for Satellite Imagery

HadoopNoSQL

/MongoDBRDBMS

Data

Manage

Simple File

Distribution

based HDFS

No schemaRelational

model

Query

ProcessMapReduce Key-value SQL

Constraint

Difficult to

process mass

science data

Difficult to

process

mathematic

al analysis

Difficult to

process

multi-

dimensional

data

Limit of Data Processing Technique

For Satellite Data

SciDB

▪ Open source high

performance DB

▪ Support over a number of

petabytes data

▪ Array data model

▪ Support Multi-Dimensional

data

▪ Support Data version

▪ Support R, Python lan.


Visualize MODIS by SciDB & GDAL

H08v05-the western United States

Test Data(MODIS) : MOD13A3

EVI : Enhanced Vegetation Index

NDVI; Normalized Difference Vegetation Index

H8V5


Visualize MODIS by SciDB & GDAL

2001-04-01, NDVI, H12V9 2004-04-01, NDVI, H12V9


2. 연구결과 빅데이터분석기술

∙ Step1. target 지역 선정 : 물동량이 많으며, 다양한 환경의 도시의 최근영상을 획득

∙ Step2. Labeling : 동일 지역의 서로 다른 날짜를 비교하여 선박으로 추정되는부분을 labeling하여 좌상, 우하의 위치 정보(+margin) 추출

∙ Step3. Answer sheet 제작 : 육지 및 바다와 같이 선박으로 분류될 수 없도록오답지 정보를 추출

∙ Step4. Training : 3,4 번 데이터를 바탕으로 훈련 데이터로 입력하여 모델 학습

∙ Step5. Prediction: 선박으로 추정되는 부분을 전체 지역에서 구분하여 표시

『목표 : SAR 영상 내에서 선박 추정』

Training set (True) Training set (False)

∙ Develop Second Segmentation Algorithm

Item Content

Segmentation 1 type(Ship in the water)

Data SourceSentinel-1 SAR Image

https://earthdata.nasa.gov

Resolution5 x 20 m Spatial Resolution

(Swath 250 km)

Training Set Annotated in 10,000 x 10,000 image

Training Time About 4 hours

Model U-net (10 layers)

Develop Env.

Lan. : Python

FW : Keras over Tensorflow

HW : 4 x GPU

(NVIDIA GeForce GTX TITAN X)

Ship+15(margin)


■ SAR 영상에서배세그멘테이션

2017.6.3( Shanghai, China )

∙ Result – Original Data

2017.6.27( Shanghai, China )



배경흰색으로

∙ Result – Predicted Image

2017.6.3( Shanghai, China ) 2017.6.27( Shanghai, China )



∙ Result – Merge Original & Segmentation Image

2017.6.3( Shanghai, China ) 2017.6.27( Shanghai, China )



∙ 노이즈감소와데이터부족해결을위해 data augmentation 수행

- Keras에서 지원하는 ImageDataGenerator 함수 사용

- 다양한 방식의 augmentation이 가능하나 최대한 배의 형상 유지 위해최소한의 변형만 부여

[ ImageDataGenerator 환경설정 ]

[ Original Img. (좌) Augmented Img. (우) ]



∙ 노이즈개선및정확도증가

[노란색 (augmentation 전 ) -> 초록색 (후)]



[ Panama (6/11) ] [ Hong Kong (6/4) ]

For short-term

Enhance Training model with updating training set

• And, check the reliability of our algorithm.

Integrate Deep Learning Segmentation Algorithm in SciDB

• Develop Keras-R interface module

• Import SAR image in SciDB by GDAL interface

• Visualize in R

Develop Object Detection Algorithm and integrate in our system

Distribute SciDB Data in Multi-node

For long-term

Survey target Application for verifying Bigdata Framework

Revise the Bigdata Framework for Nanosatellite Constellation

3. 향후 계획

[1] 박재필, “위성군 사례 및 연구동향”, 중소기업 상용화 지원사업과제회의, 2017.10.18

[2] 김민식, “소형 위성군 운용을 위한 데이터 동기화 모듈개발”, KSAS 2017

[3] 최연주, “Detection of ships in SAR image by using deep learning”,KSAS 2017

[4] 한상혁, “Bigdata Framework Design for Nanosatellite Constellation Using Multi-Dimensional Array Database”, COSPAR 2017

4. Reference

Contact : [email protected]

multidatabase systems and interoperability issues

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