Publication Date: 2020-10-22
Approval Date: 2020-09-23
Submission Date: 2020-08-14
Reference number of this document: OGC 20-038
Reference URL for this document: http://www.opengis.net/doc/PER/EOAppsPilot-SatCen
Category: OGC Public Engineering Report
Editor: Omar Barrilero, Adrian Luna
Title: OGC Earth Observation Applications Pilot: European Union Satellite Centre Engineering Report
COPYRIGHT
Copyright © 2020 Open Geospatial Consortium. To obtain additional rights of use, visit http://www.opengeospatial.org/
WARNING
This document is not an OGC Standard. This document is an OGC Public Engineering Report created as a deliverable in an OGC Interoperability Initiative and is not an official position of the OGC membership. It is distributed for review and comment. It is subject to change without notice and may not be referred to as an OGC Standard. Further, any OGC Public Engineering Report should not be referenced as required or mandatory technology in procurements. However, the discussions in this document could very well lead to the definition of an OGC Standard.
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- 1. Subject
- 2. Executive Summary
- 3. Terms and definitions
- 4. Overview
- 5. EO Application Description
- 6. Findings and discussion during the application integration in the platforms
- 7. Conclusions/Recommendations
- Appendix A: Revision History
1. Subject
This Engineering Report (ER) describes the achievements of the European Union Satellite Centre (SatCen) as an application provider in the OGC Earth Observation Applications Pilot and the lessons learned from the project.
2. Executive Summary
The objective of the project was for application developers to adapt an application, deploy the application and execute it on different Earth Observation (EO) data and processing platforms.
The main objective was not the integration itself, but to explore what changes are needed in the application to be integrated in a platform, the points that are working well and which ones need to be improved, what solutions/technologies are useful, and what are the limitations to be able to integrate the same application on different platforms.
SatCen has integrated in three platforms an application that computes the coherence of two Sentinel-1 SLC images. During the process, some modifications were needed in the application in order to facilitate the integration. This process and the findings and discussion are explained in detail in this ER.
The main conclusion is that building a platform-agnostic application is feasible: the SatCen application has been integrated in different platforms using the same docker image and following the approach decided during the project. The approach involved the use of Docker for containerization, the Common Workflow Language (CWL) for describing the application, and common metadata.
Although the Pilot can be considered successful, it has also brought to light some points that need to be improved, which are described in detail throughout this document.
2.1. Document contributor contact points
All questions regarding this document should be directed to the editor or the contributors:
Contacts
Name | Organization | Role |
---|---|---|
Omar Barrilero |
SatCen |
Editor |
Adrian Luna |
SatCen |
Editor |
2.2. Foreword
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. The Open Geospatial Consortium shall not be held responsible for identifying any or all such patent rights.
Recipients of this document are requested to submit, with their comments, notification of any relevant patent claims or other intellectual property rights of which they may be aware that might be infringed by any implementation of the standard set forth in this document, and to provide supporting documentation.
3. Terms and definitions
For the purposes of this report, the definitions specified in Clause 4 of the OWS Common Implementation Standard OGC 06-121r9 shall apply. In addition, the following terms and definitions apply.
- ● Coherence
-
The coherence refers to the amplitude of the complex correlation coefficient between two SAR images. It is a measure of the similarity of two Synthetic Aperture Radar (SAR) images.
- ● Container
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A standardized unit of software (Docker).
- ● SNAP Graph
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A set of operators that define a processing chain in SNAP. SNAP, the Sentinel Application Platform, is common architecture for all Sentinel Toolboxes.
3.1. Abbreviated terms
-
ADES Application Deployment and Execution Service
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AI Artificial Intelligence
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AOI Area Of Interest
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AP Application Package
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AWS Amazon Web Service
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BPEL Business Process Execution Language
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CFP Call For Participation
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CWL Common Workflow Language
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DEM Digital Elevation Model
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DWG Domain Working Group
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EMS Execution Management Service
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EO Earth Observation
-
EP Exploitation Platform
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ER Engineering Report
-
ESA European Space Agency
-
FUSE Filesystem in Userspace
-
GCP Google Cloud Platform
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GDAL Geospatial Data Abstraction Library
-
GUI Graphical User Interface
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JSON JavaScript Object Notation
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MEP Mission Exploitation Platform
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OGC Open Geospatial Consortium
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OWC OWS Context
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REST REpresentational State Transfer
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S1 Sentinel-1
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S2 Sentinel-2
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SAR Synthetic Aperture Radar
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SLC Single Look Complex
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SNAP SeNtinel Application Platform
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TEP Thematic Exploitation Platform
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TIE Technology Integration Experiments
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TOI Time Of Interest
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UI User Interface
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URI Uniform Resource Identifier
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URL Uniform Resource Locator
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VM Virtual Machine
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WKT Well-Known Text
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WCS Web Coverage Service
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WFS Web Feature Service
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WPS Web Processing Service
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WPST Web Processing Service Transactional
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XML eXtensible Markup Language
4. Overview
Section 5 introduces the application developed by SatCen to be integrated into the Earth Observation data and processing platforms.
Section 6 presents the lessons learned during the project as an application developer. All the findings and discussions are explained in detail.
Section 7 provides the conclusions and recommendations for future work.
5. EO Application Description
5.1. Overview
The application developed by SatCen and integrated into the different platforms aims to identify changes between two Sentinel-1 (SAR) images acquired at different times but acquired with very similar acquisition conditions (sometimes also called interferometric conditions), by computing the "Amplitude" or calibrated backscatter and the "Coherence" between the two images. The coherence refers to the amplitude of the complex correlation coefficient between two SAR images.
The analysis of the coherence is very useful for change detection. Weather and illumination independence make SAR acquisitions particularly interesting for change detection and monitoring scenarios, especially in certain areas of the world or periods of time where weather conditions prevent the use of optical imagery. Additionally, the coherence maps have been found to be of great utility in detecting changes that occur on the ground and that are not detectable by using only the amplitude or backscatter signal.
5.2. Inputs
The inputs of the application are two Sentinel-1 SLC images that should be acquired in the same conditions (same relative orbit). Also, it is possible to define the area of interest in order to compute the coherence only over the desired area. Some other ancillary data is needed, but it is not directly selected by the user: the application automatically will look for some orbit and DEM (Digital Elevation Model) files to download them.
5.3. Processing
The application is written in Python and it uses SNAP and GDAL internally. The main process for the coherence calculation is carried out in SNAP, but some functions of GDAL are also used.
The main steps in the processing are:
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Read parameters and check them
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Split inputs and run SNAP graphs
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Mosaic the intermediate outputs
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Generate metadata
5.4. Outputs
The output is a GeoTIFF image with three bands:
-
Image 1 backscatter
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Image 2 backscatter
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Coherence
The output can be the input for other algorithms (e.g. thresholding and algorithms based on Artificial Intelligence, …) for automatic change detection but is also useful for a visual analysis. A usual Red-Green-Blue (RGB) color composite (R:Image1 backscatter, G:Image2 backscatter, B:Coherence) facilitates the interpretation of the results, since it allows the interpreter to assign the different colors of the pixels to different conceptual categories, for example:
-
White: High backscatter in both images and high coherence (e.g. buildings, vehicles). It can be interpreted as "No Change".
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Yellow: High backscatter in both images but low coherence (e.g. changes on buildings, parking lots, containers). It can be interpreted as "Change" or "Activity".
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Red: High backscatter in first image, low backscatter in second image, low coherence (e.g. building destruction, ship in the first image but not in the second). It can be interpreted as "Change", usually a built-up element present on date1 but not on date2.
-
Green: High backscatter in the second image, low backscatter in first image, low coherence (e.g. building destruction, ship in the second image but not in the first). It can be interpreted as "Change", usually a built-up element present on date2 but not on date1.
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Blue: Low backscatter in both images but high coherence (e.g. roads, airport).