Difference between revisions of "Image Classification Tutorial using Orfeo Toolbox"

Revision as of 12:47, 5 December 2010

Disclaimer

The information contained in this wiki is part of a project deliverable for a 4000 level Geomatics course at Carleton University. Information pertaining to software tools and parameters may be different depending on your application and software version. Landsat imagery was used in this tutorial however other image sources may be used to conduct this tutorial. Geospatial processing time may vary depending on computer configuration and size of data used.

Introduction

A fundamental aspect of image interpretation and analysis is the classification of land features in order to produce land cover maps. Typically produced using satellite imagery, classification is the process of sorting image pixels of unknown identity into groups based on points of known identity (Davidson, 2010). The user attempts to classify various features and/or land cover classes of interest using visual interpretation to group homogeneous pixels to create a thematic map (Canada Centre for Remote Sensing, 2008). Classification procedures can be broken into two categories based on the method used. Unsupervised classification, which determines natural statistical groupings within multispectral data based on the separations between means, is often used when little is known about the study area. Spectral classes are determined using statistical information followed by the user matching the classes to the land covers. Several algorithms can be employed to perform an unsupervised classification to determine the natural pixel groupings in the image. Supervised classification which can be seen as the reverse of unsupervised classification, involves the selection of land cover groupings to be mapped in combination with the delineation of training pixels for each class, the user has greater control over the procedure and has input on every step of the classification process compared to the unsupervised classification method. Once the training areas are delineated, a classifier such as maximum likelihood is used to assign all unknown pixels to the class whose training data they resemble most. The purpose of this tutorial is to emulate the supervised classification procedure using Orfeo Toolbox (OTB) and export the classification results in order to create thematic maps.

Background

This tutorial is conducted using Orfeo Toolbox. Orfeo Toolbox is an open source remote sensing image processing software with a goal of facilitating the development of new algorithms and validation procedures. It is a multiplatform, free to use software with a C++ library containing a multitude of pre-processing and image analysis algorithms. The graphical user interface or GUI provides non programmers with the ability to visually comprehend and analyze the procedures as well as interact with the available parameters. By including several well known algorithms and tools for free, OTB encourages research by stressing the importance of understanding how algorithms work, as their slogan puts it, OTB is not a black box (Orfeo Toolbox, 2010). OTB offers functionalities for remote sensing image processing such as but not limited to image filtering, feature extraction, change detection and classification.

Objective

In this tutorial you will learn how to apply your existing remote sensing skills to generate land-cover data using a supervised classification procedure in order to display the results in Google Earth using a combination of OTB and Quantum GIS (QGIS) with the Grass plugin. The final outcome is a KML file containing the land-cover polygons extracted from the supervised classification. Free and open source software and freely available Landsat imagery will be used to demonstrate an image classification application without using proprietary software.

Method

Data Access

The first step in this tutorial is to download the free imagery available online you will be using. Multiple sources are available online but for the purpose of this tutorial, the imagery was retrieved from Geobase, an up-to-date database of quality geospatial data for all of Canada. Landsat orthoimages acquired in August 2001 with less than 10% could covering the nation's capital were downloaded and will be utilized for the supervised classification. Table 1 consists of a snapshot of the metadata for the images used in this tutorial.

**Table 1. Snapshot of the metadata for the five Landsat images obtained from Geobase**
Dataset	Data Type
Product ID	016026_0100_010825_L7Media_id
Projection	UTM_zone - 18
Datum	NAD83 (CSRS)
Number of Pixels	7602
Raw Image Number	LE7016026000123750 Path - 016
Acquisition Date	2001/08/25
Horizontal Positional Accuracy	13 metres
Elevation Model Accuracy	51 metres

Orfeo Toolbox GUI

Orfeo Toolbox

Installation

View Bands in RGB

Create your Area of Interest

Clustering

Image Classification

Quantum GIS

Image Filter and Data Export

Conclusion

Congratulations, you have completed a supervised image classification using open source software and freely available Landsat imagery. Although many remote sensing applications rely on propriety software because they have been tested and proven, this tutorial demonstrates that OTB software although not yet mainstream, provides an open source alternative in an industry where large sums of money is spent purchasing proprietary software. While the SVM algorithm provided visually pleasing results, the incorporation of other supervised classifiers in OTB is needed to suit the different needs of various projects. By incorporating QGIS, the user has greater capabilities to manage and export data. QGIS proved to be an effective open source software to export data to KML and filtering imagery. Although OTB contains an option to export to KML, QGIS offers the opportunity to convert to vector which enhances the viewing capabilities of geospatial datasets in Google Earth. Overall, OTB offers valuable tools for image classification; it is however designed to process high resolution imagery, however high resolution is often expensive and not freely available therefore falling outside the scope of this exercise.

References

Davidson, A. (2010). A Davidson's slides on Image Classification. GEOM 4003: Remote Sensing of the Environment.

Orfeo Toolbox. (2010). Orfeo Toolbox is not a black box.. Retrieved November 19, 2010 from http://www.orfeo-toolbox.org/otb/

Tutorial: Fundamentals of Remote Sensing Image interpretations & analysis - Image Classification. (2008). Canada Centre for Remote Sensing. Retreived November 19, 2010 from http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/chapter4/07_e.php

Zhang, R. & Ma, J. (2008). An improved SVM method P-SVM for classification of remotely sensed data. International Journal of Remote Sensing 29, 6029-6036

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 {| border="1" cellpadding="5" cellspacing="0" align="center"
-|+'''Table 1. Landsat 7 Orthoimage''' |align="left"
+|+'''Table 1. Snapshot of the metadata for the five Landsat images obtained from Geobase'''
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 ! style="background:#efefef;" | Dataset