Monteverdi - Remote Sensing Software from Educational to Operational Context

Transcription

1 Remote Sensing for Science, Education, Rainer Reuter (Editor) and Natural and Cultural Heritage EARSeL, 2010 Monteverdi - Remote Sensing Software from Educational to Operational Context Manuel GRIZONNET, and Jordi INGLADA CNES 18, avenue Edouard Belin Toulouse Cedex 09 - France Abstract. The Orfeo Toolbox (OTB) is an Open Source Remote Sensing Image Processing software toolkit developed by CNES which aims at gathering a large set of state of the art building blocks for processing chains. The Monteverdi integrated application is based on OTB and provides a userfriendly graphical interface to many of the algorithms available in the OTB library. The modular architecture of the software allows to easily extend the application with custom algorithms seamlessly integrated in the main application. All these features make Monteverdi a candidate to provide quick and efficient solutions in educational contexts. It has already been used for several remote sensing trainings and capacity building activities. This paper will present OTB/Monteverdi in terms of available functionalities, but also in terms of philosophy of openness and future evolution. Keywords: Remote sensing, Orfeo Toolbox, end user application Introduction One common problem when working with satellite images is the gap between algorithms and techniques learned in classrooms and methods which are used in operational situations. Some of these methods are sometimes available in commercial software used in professional contexts but they can require some fine tuning for which an in-depth understanding of the theory is necessary. For example, some of them might not scale well when asked to process billions of pixel images, some would be too slow to enable interactivity, and some would try to guess (often wrongly) what the end user needs. It arises that there is clearly the need for easily available applications where end users are able to know in detail the algorithms and are also able to tune and adapt them. It covers a large scope of users, from students to professional users, and lots of algorithm categories. The Monteverdi application based on the Orfeo Toolbox library is a first answer and contribution for bringing together these needs. 1. Remote sensing applications for education 1.1. Real applications on real images Production software is often robust but do not need fine tuning of several parameters requiring an in depth knowledge of the underlying algorithms. It arises that users are rapidly lost in a large number of generic processing blocks that they are unable to compare or benchmark. Moreover their processing chains are not easily extensible and force them to add other software which makes their process complex. As a consequence the validation of each step results in a huge contrast between professional results and algorithms that are available in the labs. This increases the difficulty to apply them and reproduce results on different data From classroom to operations In operational contexts, users of remote sensing data often make reference to what they call "the real world of remote sensing process". This real world, by opposition with theory, refers generally

2 to specific needs of an operator who has to produce results in "non academic" situations, in a critical time frame, or with few available data. These situations imply the need to adapt theoretical results and to be able to create efficient and reproducible processes. That is why algorithms and applications need to be efficient, well tested and in adequacy with teaching methods. As a consequence, there is clearly the need for flexible and powerful applications that people can study and modify without restriction, and which can be also redistributed easily. As a result, the demand for well designed programs and homogeneous tools which will cover the use from schools to private companies is increasing. In the frame of remote sensing image processing, the Orfeo Toolbox library and the front end application Monteverdi provide a large set of cutting edge algorithms in response to this need. 2. Presentation of Monteverdi 2.1. Detail of a Monteverdi process Monteverdi allows users to build remote sensing processing chains and execute them at any moment during the process. It allows to chain dynamically modules' inputs and outputs behind a common and coherent interface. Historically, Monteverdi was designed after a request from CNES' Strategy and Programs Office in order to provide an integrated application for capacity building activities (teaching, simple image manipulation, etc.) with the possibility to easily integrate new processing modules. Let's see a first example of the power of the application. Figure 1 shows a screenshot of Monteverdi's main window and presents thematic sections which are available and where you can see the different modules which have been set up for the processing. The first step is to access to input data via the reader module (File Open dataset) which are automatically registered on the process tree. When you choose to use a new module, you select its input data, and therefore, you build a processing pipeline sequentially and output(s) of processing modules are automatically added to the main window tree. The processing and its parameters are stored and, at any time, the user can decide to trigger or not the execution of the processing. On Figure 2, the pink button to the right of the image selection menu indicates that the image has not been generated (streamed). When the user pushes the button, the pipeline process is executed and the result image is be saved on disk computer. The next sections present some modules available in Monteverdi which cover a large scope of remote sensing techniques, from geometric processing to image analysis and creation of value added remote sensing products Geometry processing This section deals with re-projection and orthorectification of images using the available sensor models (image informations available in the meta-data are automatically read by the application). The list of available modules is: Orthorectification using the available sensor models Homologous point extraction for image co-registration (Figure 3) Automatic image registration using sensor geometry RPC to sensor model: Create a rational polynomial coefficients sensor model (RPC) for an image by manually choosing ground control points. Figure 4 shows a screenshot of the Ground control Point to geometry module. It allows to interactively select ground control points and compute a geometric model for image orthorectification. Finding the correspondence between the pixel coordinates in the image and the geographic position 750

3 is made easy by the possibility to estimate the pixel location from Open Street Map tiles (via road intersections or characteristic points of interest for example), or by importing/exporting a list of points from/to an XML file. This is very useful in case where users do not have access to sensor model informations and allow to theorically orthorectify any imagery. Figure 1. Monteverdi main window Figure 2. Streamed input data 751

4 Manuel GRIZONNET, and Jordi INGLADA: Monteverdi - Remote Sensing Software from Educational to Operational Context Figure 3. Homologous point extraction module Figure 4. GCP to sensor module 2.3. Feature extraction This module allows to create a set of layers resulting from feature extraction algorithms. The term Feature Extraction includes several techniques aiming to detect low level information from images. These features can be objects (points, lines, etc.) but they can also be measures (moments, textures, etc.). Thanks to streaming capabilities of the Orfeo Toolbox, the module is able to display a preview of each process on the region of interest to evaluate the result. Figure 5 shows an example of filtering operations with the module and a preview of the result is available to evaluate the influence of a given parameter. 752

5 2.4. Mean-shift segmentation Figure 5. Feature extraction module The Mean-shift algorithm allows the generation of a segmented image. Basically, this module could be used before processing supervised classification using Support Vector Machines (SVM). Moreover this basic module can illustrate the modularity of Monteverdi and is an excellent base to build your own Monteverdi modules. You can start from here and just change the filter in the pipeline and get a ready to use new module. Figure 6. Mean shift segmentation module 753

6 2.5. Supervised classification Monteverdi allows to generate supervised classifications based on the Support Vector Machines (SVM) algorithm and is able to produce good classification models from few examples. On Figure 7, an example of classification by SVM is illustrated. On the multispectral image, few regions of interest are selected to train the SVM. Then the entire image is classified. a) Multispectral image b) Regions of interest for learning c) Classification result Figure 7. Support Vector Machine example: on a multispectral image, four areas of interest are defined. These areas are the base for the SVM learning step. Then all pixels are classified. 3. Modularity and customization The Monteverdi application is based on a smart software architecture which allows building processing chains by selecting modules from a set of menus and takes advantage of the streaming and multi-threading capabilities of the OTB pipeline. Moreover, this architecture was designed to allow a high level of customization. Monteverdi is designed to host virtually an infinite number of modules. And on the other hand, it is very easy and straightforward to remove modules from the main application. This way,one can create a new customized application with only specific functionnalities of interest. This could be a real added value in an academic context to be able to adapt the content of the software to the audience and make the assimilation of new concepts by students easier. One can even imagine to add functionalities to Monteverdi during training sessions, and create an adaptive system which will fit to the teaching progress. 4. The Orfeo Toolbox Library 4.1. Overview In the frame of ORFEO Accompaniment Program, CNES decided to develop an open source remote sensing image processing library, which capitalizes state of the art techniques as well as recent results published in the literature. About 3000 C++ classes are already available in the current version of OTB [1] for most of the usual operations on remote sensing images. All of these operations can be combined to lead to fully functional applications. This modularity also enables replacing easily one method by another one in the processing chain to evaluate its impact on performance. Such modules include: image access: optimized read/write access for almost any of the existing remote sensing image formats, meta-data access, visualization; geometric modeling: sensor models for optical and SAR sensors, digital elevation models (DEM) access, cartographic projections, image registration, disparity map estimation; filtering: blurring, denoising, enhancement; 754

7 feature extraction: interest points, alignments, lines; image segmentation: region growing, fast marching, watershed, level sets; object extraction: road network extraction, template-based detection; classification: K-means, SVM, Markov random fields; change detection; optical/sar image calibration. Only some of these modules are available in Monteverdi but there is no restriction on the creation of new components in the future Open source As we can see, many algorithms are available and well-tested in Orfeo Toolbox [1,2] and more generally in the open-source community. Therefore, they can be integrated into an existing project at marginal cost. The main principle of the library is to carefully select open source libraries with specific functionalities and to integrate them behind an homogeneous interface. For example, the core of the OTB system is coming from ITK [3] which has proven its efficiency for medical image processing. Most of the algorithms for segmentation and registration have been well tested in this context. 5. Perspectives Monteverdi is designed to host virtually an infinite number of modules or, at the opposite, customize an application with only selected modules for specific users. This high level of customization promises the perspective to be able to answer to a wide panel of remote sensing products users. Moreover the license policy of the library which allows to use, modify or even make software based on it, encourages new developments. CNES organized a training on remote sensing and image processing in Madagascar at the end of 2009 where Monteverdi was used as the main tool. The success of the training and the very positive feed back of students were a first demonstration of the efficiency of the application as a support for remote sensing courses. Since then, Monteverdi is used regularly in some French educational institutions for technical courses (on rapid mapping for example) and in parallel CNES continues to encourage and promote developments around the application. References [1] The {ORFEO} toolbox software guide, [2] E. Christophe and J. Inglada, Open Source Remote Sensing: Increasing the Usability of Cutting-Edge Algorithms. IEEE Geoscience and Remote Sensing Society Newsletter, 9-15, March (last access: ) [3] ITK, the Insight Toolkit

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