Image Classification Tutorial using Orfeo Toolbox

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

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

Method

Data Access

Orfeo Toolbox

Installation

View Bands in RGB

Create your Area of Interest

Clustering

Image Classification

Quantum GIS

Image Filter and Data Export

Conclusion

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

Image Classification Tutorial using Orfeo Toolbox

Contents