DOCSLIB.ORG

Pragmatic Remote Sensing a Hands-On Approach to Processing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Pragmatic Remote Sensing A Hands-on Approach to Processing J. Inglada1 , E. Christophe2 1CENTRE D’ÉTUDES SPATIALES DE LA BIOSPHÈRE,TOULOUSE,FRANCE 2CENTRE FOR REMOTE IMAGING,SENSING AND PROCESSING, NATIONAL UNIVERSITY OF SINGAPORE This content is provided under a Creative Commons Attribution 3.0 Unported License IGARSS 2010, Honolulu Table of Contents 1. General introduction 2. Pre-processing 2.1 Geometric corrections 2.2 Radiometric corrections 3. Feature extraction 4. Image classification 5. Change detection IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Pragmatic Remote Sensing A Hands-on Approach to Processing J. Inglada1 , E. Christophe2 1CENTRE D’ÉTUDES SPATIALES DE LA BIOSPHÈRE,TOULOUSE,FRANCE 2CENTRE FOR REMOTE IMAGING,SENSING AND PROCESSING, NATIONAL UNIVERSITY OF SINGAPORE This content is provided under a Creative Commons Attribution 3.0 Unported License IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Why? Common problems I Reading images I Accessing metadata I Implementing state of the art algorithms to be able to extract the most information, we need to use ⇒ the best of what is available: data, algorithms,. IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy What When Why How What is Orfeo Toolbox (OTB)? In the frame of CNES ORFEO Program Goal Make the development of new algorithms and their validation easier I C++ library: provide many algorithms (pre-processing, image analysis) with a common interface I Open-source: free to use, to modify, you can make your own software based on OTB and sell it I Multiplatform: Windows, Linux, Unix, Mac IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy What When Why How A bit of History Everything begins (2006) I Started in 2006 by CNES (French Space Agency), funding several full-time developers I Targeted at high resolution images (Pleiades to be launched in 2010) but with application to other sensors I 4 year budget, over 1,000,000e recently renewed for 1 additional year (500,000e) Moving to user friendly applications (2008) I Strong interactions with the end-user community highlighted that applications for non-programmers are important I Several applications for non programmers (with GUI) since early 2008 I Several training courses (3/5-day courses) given in France, Belgium, Madagascar, UNESCO and now Hawaii IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy What When Why How Why doing that? Is it successful so far? I OTB user community growing steadily (programmers and application users) I Presented at IGARSS and ISPRS in 2008, special session in IGARSS in 2009 I CNES is planning to extend the budget for several more years I Value analysis is very positive (cf. Ohloh): re-using is powerful Why make a multi-million dollar software and give it for free? I CNES is not a software company I One goal is to encourage research: it is critical for researchers to know what is in the box I CNES makes satellites and wants to make sure the images are used I if more people have the tools to use satellite images, it is good for CNES IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy What When Why How How? How to reach this goal? Using the best work of others: do not reinvent the wheel Many open-source libraries of good quality I ITK: software architecture (streaming, multithreading), many image processing algorithms I Gdal/Ogr: reading data format (geotiff, raw, png, jpeg, shapefile, . ) I Ossim: sensor models (Spot, RPC, SAR, . ) and map projections I 6S: radiometric corrections I and many other: libLAS (lidar data), Edison (Mean Shift clustering), libSiftFast (SIFT), Boost (graph), libSVM (Support Vector Machines) all behind a common interface ⇒ IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings Application Currently I Image viewer I Image Segmentation I Image Classification (by SVM) I Land Cover I Feature Extraction I Road Extraction I Orthorectification (with Pan Sharpening) I Fine registration I Image to database registration I Object counting I Urban area detection I more to come IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings Components available Currently I Most satellite image formats I Geometric corrections I Radiometric corrections I Change detection I Feature extraction I Classification Huge documentation available I Software Guide (+600 pages pdf), also the online version I Doxygen: documentation for developers IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings A powerful architecture Modular I Easy to combine different blocks to do new processing Scalable I Streaming (processing huge images on the flow) transparent for the user of the library I Multithreading (using multicore CPUs) also IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings But a steep learning curve for the programmer Advanced programming concepts I Template metaprogramming (generic programming) I Design patterns (Factory, Functors, Smart Pointers, ...) Steep learning curve solution from scratch learning OTB Effort TaskIGARSS complexity 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings Ask questions As for everything: easier when you’re not alone I Much easier if you have somebody around to help! I We didn’t know anything not so long ago... I Not surprising that most software companies now focus their offer on support: help is important IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings Making it easier for the users: Monteverdi Module architecture I Standard input/output I Easy to customize for a specific purpose I Full streaming or caching the data I Each module follows a MVC pattern IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings Making it easier for the users: Monteverdi IGARSS 2010, Honolulu Introduction to the Orfeo Toolbox Applications and librairy Components Architecture But Monteverdi Bindings Bindings: access through other languages Not everybody uses C++! I Bindings provide an access to the library through other languages I Python: available I Java: available I IDL/Envi: cooperation with ITT VIS to provide a method to access OTB through idl/envi (working but no automatic generation) I Matlab: recent user contribution (R. Bellens from TU Delft) I Other languages supported by Cable Swig might be possible (Tcl, Ruby?) IGARSS 2010, Honolulu Introduction Atmospheric corrections Pansharpening Image radiometry in ORFEO Toolbox From digital number to reflectance J. Inglada1 , E. Christophe2 1CENTRE D’ÉTUDES SPATIALES DE LA BIOSPHÈRE,TOULOUSE,FRANCE 2CENTRE FOR REMOTE IMAGING,SENSING AND PROCESSING, NATIONAL UNIVERSITY OF SINGAPORE This content is provided under a Creative Commons Attribution 3.0 Unported License IGARSS 2010, Honolulu Introduction Atmospheric corrections Pansharpening Introduction Purpose of radiometry I Go back to the physics from the image 6S I Using 6S library: http://6s.ltdri.org/ I Library heavily tested and validated by the community I Translation from the Fortran code to C I Transparent integration to OTB for effortless (almost!) corrections by the user IGARSS 2010, Honolulu Introduction Atmospheric corrections Pansharpening DN to lum lum to ref ToA to ToC Adjacency effects Hands On Atmospheric corrections: in four steps DN to Lum to TOA to Adjacency Lum Refl TOC Look like a pipeline This is fully adapted to the pipeline architecture of OTB IGARSS 2010, Honolulu Introduction Atmospheric corrections Pansharpening DN to lum lum to ref ToA to ToC Adjacency effects Hands On Digital number to luminance k k X LTOA = + βk αk Goal k I LTOA is the I Transform the digital number in the incident image into luminance luminance (in 2 1 1 W .m− .sr − .µm− ) Using the class I Xk digital number otb::ImageToLuminanceImageFilter I αk absolute filterImageToLuminance->SetAlpha(alpha); calibration gain filterImageToLuminance->SetBeta(beta); for channel k I βk absolute calibration bias for channel k IGARSS 2010, Honolulu Introduction Atmospheric corrections Pansharpening DN to lum lum to ref ToA to ToC Adjacency effects Hands On How to get these parameters? From metadata I Sometimes, the information can be present in the metadata but. I the specific format has to be supported (Spot, Quickbird, Ikonos are and more on the way) From an ASCII file VectorType alpha(nbOfComponent); alpha.Fill(0); std::ifstream fin; fin.open(filename); double dalpha(0.); for( unsigned int i=0 ; i < nbOfComponent ; i++) { fin >> dalpha; alpha[i] = dalpha; } fin.close(); IGARSS 2010, Honolulu Introduction Atmospheric corrections Pansharpening DN to lum lum to ref ToA to ToC Adjacency effects Hands On Luminance to reflectance k k π.LTOA Goal ρTOA = k ES .cos(θS).d/d0 I Transform the luminance into the I rhok reflectance reflectance TOA I θS zenithal solar angle Using the class otb::LuminanceToReflectanceImageFilter k and setting parameters: I ES solar illumination out filterLumToRef-> of atmosphere at a SetZenithalSolarAngle(zenithSolar); distance d0 from the Earth filterLumToRef-> SetDay(day); filterLumToRef->
Recommended publications
  • Automatic Generation of a 3D City Model

    Automatic Generation of a 3D City Model

    UNIVERSITY OF CASTILLA-LA MANCHA ESCUELA SUPERIOR DE INFORMÁTICA COMPUTER ENGINEERING DEGREE DEGREE FINAL PROJECT Automatic generation of a 3D city model David Murcia Pacheco June, 2017 AUTOMATIC GENERATION OF A 3D CITY MODEL Escuela Superior de Informática UNIVERSITY OF CASTILLA-LA MANCHA ESCUELA SUPERIOR DE INFORMÁTICA Information Technology and Systems SPECIFIC TECHNOLOGY OF COMPUTER ENGINEERING DEGREE FINAL PROJECT Automatic generation of a 3D city model Author: David Murcia Pacheco Director: Dr. Félix Jesús Villanueva Molina June, 2017 David Murcia Pacheco Ciudad Real – Spain E-mail: [email protected] Phone No.:+34 625 922 076 c 2017 David Murcia Pacheco Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". i TRIBUNAL: Presidente: Vocal: Secretario: FECHA DE DEFENSA: CALIFICACIÓN: PRESIDENTE VOCAL SECRETARIO Fdo.: Fdo.: Fdo.: ii Abstract HIS document collects all information related to the Degree Final Project (DFP) of Com- T puter Engineering Degree of the student David Murcia Pacheco, tutorized by Dr. Félix Jesús Villanueva Molina. This work has been developed during 2016 and 2017 in the Escuela Superior de Informática (ESI), in Ciudad Real, Spain. It is based in one of the proposed sub- jects by the faculty of this university for this year, called "Generación automática del modelo en 3D de una ciudad".
  • Oracle Spatial 11G Release 2 New Features Deep Dive Siva Ravada Director of Development, Oracle Spatial

    Oracle Spatial 11G Release 2 New Features Deep Dive Siva Ravada Director of Development, Oracle Spatial

    Title of Presentation Oracle Spatial 11g Release 2 New Features Deep Dive Siva Ravada Director of Development, Oracle Spatial Michael Smith US Army Corps of Engineers Agenda • Introduction to Oracle Spatial <Insert Picture Here> • Spatial Engine • SQL Developer • GeoRaster • Network Data Model • Moving Objects • 3D Support Oracle Spatial Features – Oracle Locator: Feature of Oracle Database XE, SE, EE – Oracle Spatial: Priced option to HTTP Oracle Database EE Fusion Middleware – MapViewer*: Java application and map rendering feature of Fusion MapViewer Middleware – Bundled Map Content: Major roads, JDBC administrative boundaries (city, county, state, country) - worldwide Oracle Database coverage from Navteq Oracle Locator Oracle Spatial Bundled Map Content Supports all Geospatial Data types Polygons (admin, sales territories, Locations high risk zones) Networks (points of interest) (roads, utilities) Data Imagery 3D data type (satellite imagery) Topology (city models) (data provider) LIDAR Data Type TIN Data Type Open Platform for Leading Applications and Tools MapInfo Leica ADE Bentley Manifold Oracle Spatial 11g Enabled Applications Scrollable, Interactive Maps Spatial Web Services 3D, Point Clouds, and LIDAR Geocoding & Routing Oracle BI Dashboards Raster Imagery Spatial Engine Spatial on Exadata • We achieve performance improvements based on the exploitation of the processing power, bandwidth, and parallelism of the Exadata machine • Parallel query and partitioning help improve Spatial performance • All the row level functions are also parallel enabled to take advantage of parallel query • Speedups of up to 40 times (compared to a 1-core CPU box) for the typical SDO_ANYINTERACT spatial query • Spatial predicates are not currently pushed into the Exadata storage nodes 9 Spatial Index Parallel Creation on Exadata • Build a Local Spatial Index on Partitioned Table: each slave can build a different index partition in parallel.
  • GRASS GIS Loves Lidar FOSS4G NA 2016

    GRASS GIS Loves Lidar FOSS4G NA 2016

    GRASS GIS loves lidar FOSS4G NA 2016 Vaclav Petras (Vashek) Anna Petrasova, Helena Mitasova Center for Geospatial Analytics May 5, 2016 available at wenzeslaus.github.io/grass-lidar-talks Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 1 / 31 GRASS GIS I all in one I hydrology modeling, image segmentation, point clustering, . I from small laptops to supercomputers I Raspberry Pi, Windows, Mac, GNU/Linux, GNU/Hurd, FreeBSD, IBM AIX I learn now, use forever I over 30 years of development and interface refinement I probably used more than you think I similarly to C/C++ is often not latest release 7.0.4 mentioned but is somewhere in there Sunday, May 1, 2016 Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 2 / 31 CLI Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 3 / 31 GUI Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 4 / 31 Python Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 5 / 31 Python versus CLI Documentation, Command Line (Shell, Bash, cmd.exe): r.in.lidar input=points.las\ output=elevation-e Python: from grass.script import run_command run_command('r.in.lidar', input="points.las", output="elevation", flags='e') Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 6 / 31 Module GUI Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 7 / 31 Graphical Modeler Vaclav Petras (NC State University) GRASS GIS loves lidar May 5, 2016 8 / 31 Points I collected by lidar I generated by Structure
  • Architecting Location Intelligence Platforms Using Open-Source Components

    Architecting Location Intelligence Platforms Using Open-Source Components

    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021 XXIV ISPRS Congress (2021 edition) ARCHITECTING LOCATION INTELLIGENCE PLATFORMS USING OPEN-SOURCE COMPONENTS A. BENAHMED DAHO 1, H. BEKHELIFI 1 1 TransformaTek, SARL (LLC), Ain Temouchent, Algeria - (ali.benahmeddaho, hatem.bekhelifi)@transformatek.dz Commission IV, WG IV/6 KEY WORDS: GIS, Cloud Architecture, Location intelligence, Agile approach, GeoNode, Source control. ABSTRACT: Location Intelligence is an emerging application of geospatial industry. It allows professionals from different business domains (finance, disaster management, retail, health) and with minimum expertise in GIS technologies to use efficiently many spatial analysis tools and algorithms to solve their day-to-day problems. One of the major characteristics of Location Intelligence is the use of multisource and alternative data either user ingested or directly available in the platform (by geo-enrichment). In this contribution we investigate the architectural challenges raised by the development of this kind of platforms, particularly in term of functionalities, use of open-source components, management practices, deployment on the Cloud and source code control. To validate our assumptions, we built a new platform, named “Geoweba”, based on the GeoNode Project and having as objective to disrupt the Algerian consulting market. The resulting platform is deployed on the internet and freely available for users. In this contribution we demonstrate that our development approach based on Agile principles and open-source components gives a good result, mainly in the early stages when just a Minimum Viable Product (MVP) is needed for market validation of the idea. 1. INTRODUCTION Location intelligence software, also called spatial intelligence Functioning software, is a business intelligence solution that provides location Sprint Deliverables analytics to identify the relationship between certain objects TODO 2-4 Weeks based on their physical locations.
  • Sphinx Documentation Release 1.5.4

    Sphinx Documentation Release 1.5.4

    Sphinx Documentation Release 1.5.4 Georg Brandl Apr 02, 2017 Contents 1 Introduction 1 1.1 Conversion from other systems....................................1 1.2 Use with other systems........................................2 1.3 Prerequisites..............................................2 1.4 Usage..................................................2 2 First Steps with Sphinx 3 2.1 Install Sphinx..............................................3 2.2 Setting up the documentation sources................................3 2.3 Defining document structure.....................................4 2.4 Adding content.............................................5 2.5 Running the build...........................................5 2.6 Documenting objects..........................................5 2.7 Basic configuration...........................................6 2.8 Autodoc.................................................7 2.9 Intersphinx...............................................7 2.10 More topics to be covered.......................................8 3 Invocation of sphinx-quickstart9 3.1 Structure options............................................9 3.2 Project basic options..........................................9 3.3 Extension options........................................... 10 3.4 Makefile and Batchfile creation options............................... 10 3.5 Project templating........................................... 11 4 Invocation of sphinx-build 13 4.1 Environment variables......................................... 15 4.2 Makefile options...........................................
  • 3D Point Clouds in Postgresql/Postgis for Applications in GIS and Geodesy

    3D Point Clouds in Postgresql/Postgis for Applications in GIS and Geodesy

    3D Point Clouds in PostgreSQL/PostGIS for Applications in GIS and Geodesy Theresa Meyer a and Ansgar Brunn b University of Applied Sciences Wuerzburg-Schweinfurt, Roentgenring 8, Wuerzburg, Germany Keywords: 3D Point Clouds, Point Cloud Tiling, Geodatabase, GIS, 3D Applications. Abstract: Besides the common approach of an exclusively file based management of 3D point clouds, meanwhile it is possible to store and process this special type of massive geodata within spatial database systems. Users benefit from the general advantages of database solutions and especially from the potentials of a combined analysis of original 3D point clouds, 2D rasters, 3D voxel stacks and 2D and 3D vector data in order to gain valuable geo- information. This paper describes the integration of 3D point clouds into an open source PostgreSQL/PostGIS database using the Pointcloud extension and functions of the Point Data Abstraction Library (PDAL). The focus is on performing three-dimensional spatial queries and the evaluation of different tiling methods for the organization of 3D point clouds into table rows, regarding memory space, performance of spatial queries and effects on interactions between point clouds and other GIS features within the database. A new approach for an optimized point cloud tiling, considering the individual geometric characteristic of a 3D point cloud, is presented. The results show that an individually selected storage structure for a point cloud is crucial for low memory consumption and high-performance 3D queries in PostGIS applications, taking account of its three-dimensional spatial extent and point density. 1 INTRODUCTION cloud formats enable a lossless transport of up to bil- lions of 3D point coordinates (X,Y,Z) and dozens of 3D point clouds are increasingly gaining importance additional point attributes such as Intensity, Return for various applications mainly in fields of geodesy, Number, Red, Green, Blue, NIR, Classification and geoinformatics, architecture and archaeology.
  • Open Source Remote Sensing of ORFEO Toolbox and Its Connection to Database of Postgis with NIX File Importing

    Open Source Remote Sensing of ORFEO Toolbox and Its Connection to Database of Postgis with NIX File Importing

    Korean Journal of Remote Sensing, Vol.26, No.3, 2010, pp.361~371 Open Source Remote Sensing of ORFEO Toolbox and Its Connection to Database of PostGIS with NIX File Importing Kiwon Lee† and Sanggoo Kang Dept. of Information Systems Engineering, Hansung University Abstract : In recent, interests regarding open source software for geo-spatial processing are increasing. Open source remote sensing (OSRS) is regarded as one of the progressing and advanced fields in remote sensing. Nevertheless, analyses or application cases regarding OSRS are not enough for general uses or references. In this study, three kinds of OSRS software in consideration of international popularity, types of functionalities, and development environments are taken into account: OSSIM, Opticks, and ORFEO Toolbox (OTB). First, functional comparison with respect to these is carried out on the level of the preliminary survey. According to this investigation, OTB is chosen as the most applicable OSRS software in this study. Running on OTB, NIX format importing module and database connecting module are implemented for widely general uses and further application. As for an example case, airborne image of NIX format is used to region growing segmentation algorithm in OTB, and then the results are stored and retrieved in PostGIS database to test implemented modules. Conclusively, local customization and algorithm development using OSRS software are necessary to build on-demand applications from the developers’ viewpoint. Key Words : NIX format, Open Source Remote Sensing (OSRS), ORFEO, PostGIS, Segmentation. 1. Introduction information, or processors, as well as various kinds of mashup applications of Google Maps API Since the mid-2000s, open source geo-spatial (Application Programming Interface) in Purvis et processing software has been regarded as one of al.(2006), Park and Lee (2008) and Park and Lee important progresses in the geo-spatial communities (2009).
  • The Enmap-Box—A Toolbox and Application Programming Interface for Enmap Data Processing

    The Enmap-Box—A Toolbox and Application Programming Interface for Enmap Data Processing

    Remote Sens. 2015, 7, 11249-11266; doi:10.3390/rs70911249 OPEN ACCESS remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Technical Note The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing Sebastian van der Linden *, Andreas Rabe, Matthias Held, Benjamin Jakimow, Pedro J. Leitão, Akpona Okujeni, Marcel Schwieder, Stefan Suess and Patrick Hostert Humboldt-Universität zu Berlin, Geography Department, Unter den Linden 6, 10099 Berlin, Germany; E-Mails: [email protected] (A.R.); [email protected] (M.H.); [email protected] (B.J.); [email protected] (P.J.L.); [email protected] (A.O.); [email protected] (M.S.); [email protected] (S.S.); [email protected] (P.H.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +49-30-2093-6872; Fax: +49-30-2093-6845. Academic Editors: Michael Rast, Saskia Foerster, Véronique Carrere, Karl Staenz, Lenio Soares Galvao, Richard Müller and Prasad S. Thenkabail Received: 29 May 2015 / Accepted: 27 August 2015 / Published: 1 September 2015 Abstract: The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing access to recent approaches for imaging spectroscopy data processing.
  • Open Source Point Cloud Processing HISTORY

    Open Source Point Cloud Processing HISTORY

    Open Source Point Cloud Processing HISTORY • Software APIs and tools for manipulating ASPRS LAS data • Started in 2007, first release in 2008 • 20+ releases since 2007 • BSD license OPEN SOURCE • Community-driven • Public source repository • Public bug tracker • Public mailing list • Liberal license SCOPE • Feature-rich • Shoulders (GDAL, LASzip, Boost) • Multi-platform (Win32/64, OS X, Linux) • Multi-language (C++ - C/Python/.NET) AUDIENCE • Software developers • System integrators • Processing pipelines - horizontal scaling SOFTWARE • Cadcorp SIS Desktop • SAGA GIS • Myriax Eonfusion • ERDAS LPS/eATE • LASERDATA LIS • Safe FME • LizardTech LiDAR • TopoDOT Point Cloud Compressor Processing Tool Suite http://trac.liblas.org/wiki/WhoUsesLibLAS PROJECT ACTIVITY • 11 commiters • 119 mail list subscribers • 4-12 IRC members #liblas - irc.freenode.net • 60 bug tracking members • 200+ bugs filed SPONSORSHIP • Iowa Department of Natural Resources • LizardTech • US Army Corps Cold Regions Research and Engineering Laboratory FEATURES • Filtering • Transformation • Reprojection (GDAL) • Indexing and Tiling • Compression (LASzip) FORMAT SUPPORT • ASPRS LAS 1.0, 1.1, 1.2, 1.3 (points only) • LASzip - compressed LAS (read/write) • TerraSolid .bin (read) • Oracle Point Cloud (write) • ASCII (read/write) FILTERING FILTERING $ las2las --input in.las \ --output out.las \ --drop-intensity ">=1000" \ --keep-scan-angle "<=15" \ --keep-classes 2 COLOR ASSIGNMENT $ las2las -i input.las \ --color-source image.img \ --output output.las \ --file-format 1.2 \ --point-format 3 \ --color-source-scale 256 \ --color-source-bands 3 1 2 REPROJECTION $ las2las --input epsg26915.las \ --output wgs84.las \ --scale 0.000001 \ 0.000001 \ 0.01 --a_srs EPSG:26915 \ --t_srs EPSG:4326 VERTICAL TRANSFORM $ las2las --input srs.las \ --t_srs EPSG:26915+5703 --output navd88.las ADDING VLRs $ las2las --input in.las \ --output out.las \ --add-vlr CUSTOM_VLR \ 42 "A VLR description"\ "myfile.vlr" PYTHON >>> from liblas import file >>> f = file.File('file.las', mode='r') >>> for p in f: ..
  • Orfeo Toolbox (OTB) from Satellite Images to Geographic Information

    Orfeo Toolbox (OTB) from Satellite Images to Geographic Information

    Introduction Applications and librairy Roadmap Extra Orfeo Toolbox (OTB) from satellite images to geographic information "Orfeo Toolbox is not a black box" FOSS4G 2009 www.orfeo-toolbox.org Introduction Applications and librairy Roadmap Extra What When Why How Outline 1 Introduction What is it? A bit of history Why doing that? How? 2 Applications and librairy Components Architecture But steep learning 3 What’s coming next? Monteverdi GIS integration Bindings GPU and clusters FOSS4G 2009 www.orfeo-toolbox.org Introduction Applications and librairy Roadmap Extra What When Why How What is Orfeo Toolbox (OTB)? Initiated by CNES (French Space Agency) Following the feedback from SPOT satellite series In the frame of CNES ORFEO Program to prepare the launch of Pleiades Goal Make the development of new algorithms and their validation easier C++ library: provide many algorithms (pre-processing, image analysis) with a common interface Open-source: free to use, to modify (based on the CeCILL licence) Multiplatform: Windows, Linux, Unix, Mac FOSS4G 2009 www.orfeo-toolbox.org Introduction Applications and librairy Roadmap Extra What When Why How A bit of History Everything begins (2006) Started in 2006 by CNES (French Space Agency), funding several full-time developers Targeted at high resolution images (Pleiades to be launched in 2010) but with application to other sensors 4 year budget, over 1,000,000e Moving towards user friendly applications (2008) Strong interactions with the user community highlighted that applications for non-programmers are
  • Library Concept and Design for Lidar Data Processing

    Library Concept and Design for Lidar Data Processing

    LIBRARY CONCEPT AND DESIGN FOR LIDAR DATA PROCESSING Nicolas David, Clement´ Mallet, Fred´ eric´ Bretar Institut Geographique´ National (IGN) – MATIS laboratory 2-4 avenue Pasteur 94165 Saint-Mande,´ FRANCE http://recherche.ign.fr/labos/matis fi[email protected] KEY WORDS: lidar data, Point Cloud, Design, Data Management System, Processing ABSTRACT: Airborne Laser Scanning (ALS) is nowadays a very popular technology providing accurate altimetric data for remote sensing and mapping purposes. Therefore, many algorithms have been developed so far to process these data, depending on the application. Nevertheless, for researchers, it is still a challenging task to handle large amount of heterogeneous data and adapt them for their specific aim and processes. This paper reports the thoughts and the strategy developed by the MATIS laboratory of the Institut Geographique´ National (IGN) about an efficient lidar library design in order to tackle these issues. The specification of an efficient and versatile lidar file format is first discussed. The standard and current lidar file formats are first reviewed and a new one, dedicated to raw data processing with high feature modularity is presented. Besides, existing code components and libraries are reviewed with regard to their compatibility for research development. Modularity, availability and license conditions are here the main selection criteria. Then, differents strategies for large data set handling are summarized and extended by a new solution, both based on lidar strip and raw sensor topology. These workflows are illustrated through a UML activity diagram dedicated to 2D spatial query. Finally, the current status of the implementation of this federative software as well as the perspectives of development are sketched.
  • Orfeo Toolbox: Open Source Processing of Remote Sensing Images

    Orfeo Toolbox: Open Source Processing of Remote Sensing Images

    Grizonnet et al. Open Geospatial Data, Software and Standards (2017) 2:15 Open Geospatial Data, DOI 10.1186/s40965-017-0031-6 Software and Standards SOFTWARE Open Access Orfeo ToolBox: open source processing of remote sensing images Manuel Grizonnet1* , Julien Michel1, Victor Poughon1, Jordi Inglada1,2, Mickaël Savinaud3 and Rémi Cresson4 Abstract Orfeo ToolBox is an open-source project for state-of-the-art remote sensing, including a fast image viewer, applications callable from command-line, Python or QGIS, and a powerful C++ API. This article is an introduction to the Orfeo ToolBox’s flagship features from the point of view of the two communities it brings together: remote sensing and software engineering. Keywords: Remote sensing, Image processing, Open source Orfeo ToolBox (OTB) [1] is an open-source project for Thanks to its modular architecture, OTB allows fast processing satellite imagery. Built on the shoulders of the prototyping and covers the full spectrum of algorithms for open-source geospatial community, it can process high remote sensing image processing from pre-processing to resolution optical, multispectral, hyperspectral and radar advanced feature extractions methods allowing one to go images up to the terabyte scale. A wide variety of appli- from raw data to value added products. cations are available that include: ortho-rectification or Here is an incomplete list of OTB capabilities: pan-sharpening, supervised or unsupervised classifica- • Read, write, convert, extract parts of remote sensing tion, feature extraction, SAR processing. data, All of OTB algorithms are accessible from Monteverdi, • Pre-processing like ortho-rectification, radiometric C++ Quantum GIS [2], Python, the command-line or .