Image Classification Tutorial using Orfeo Toolbox - Revision history

Smitchs at 14:49, 5 March 2012

2012-03-05T14:49:29Z

Smitch: /* Conclusion */

2010-12-15T16:37:06Z

Conclusion

Smitch: /* View Bands in RGB */

2010-12-15T16:31:00Z

View Bands in RGB

Smitch: /* Installation */

2010-12-15T16:28:27Z

Installation

Smitch: /* Data Access */

2010-12-15T16:27:41Z

Data Access

206.248.172.57: /* Data Access */

2010-12-15T16:26:36Z

Data Access

Pgratton: /* Image Classification */

2010-12-07T00:17:54Z

Image Classification

Pgratton: /* View Bands in RGB */

2010-12-06T22:45:07Z

View Bands in RGB

Pgratton: /* Create your Area of Interest */

2010-12-06T22:44:50Z

Create your Area of Interest

Pgratton: /* Clustering */

2010-12-06T22:44:38Z

Clustering

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 * [http://www.carleton.ca/ Carleton University] Web site
 * [http://en.wikipedia.org/wiki/Carleton_University Carleton University Wiki]
+[[Category:Tutorials]]

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 ==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.
+Congratulations, you have completed a supervised image classification using open source software and freely available Landsat imagery. Although many remote sensing applications rely on proprietary software because they have been tested and proven, this tutorial demonstrates that OTB software, although perhaps 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 designed to process high resolution imagery, however high resolution is often expensive with restrictive licenses, therefore use of such data falls outside the scope of this exercise.
 ==References==

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 * By selecting on the Histogram tab in the image setup window, you have the option to modify the scaling parameters of the image therefore visually modifying the image to enhance natural color viewing capabilities. Having both the viewer window and setup window side by side is suggested in order to facilitate processing.
 * To navigate the image, click on your desired area in the Navigation View in the viewer window, doing so will refresh the full resolution view.
-* At this stage, it is suggested to export your RGB file comprised of all 5 bands. To do so, right click on the RGB Ottawa folder and select Export Dataset. Navigate to an appropriate folder and give the RGB file a name with its appropriate extension (i.e. ottawa.tif). Change the data type to 8bit unsigned in order to limit the size of the file.
+* At this stage, it is suggested to export your RGB file comprised of all 5 bands. To do so, right click on the RGB Ottawa folder and select Export Dataset. Navigate to an appropriate folder and give the RGB file a name with its appropriate extension (i.e. ottawa.tif). Change the data type to 8bit unsigned in order to limit the size of the file (Landsat provides a maximum of 8 bits of reflectance data).
 <br>

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 ====Installation====
-OTB is a multiplatform software which can be downloaded at http://www.orfeo-toolbox.org. The ready-to-install Windows binary package was downloaded, however, development version of OTB are available for download as well as a Quantum GIS plugin. Once OTB has been installed successfully, the software may be opened and the simple GUI is now displayed to the user.
+OTB is a multiplatform package which can be downloaded at http://www.orfeo-toolbox.org. The ready-to-install Windows binary package was downloaded, however, development version of OTB are also available for download, as well as a Quantum GIS plugin. Once OTB has been installed successfully, the software may be opened and the simple GUI is now displayed to the user.
 <br>
 ====Insert Data====
 The GUI is simple and clean making it easy to navigate between tools. Once open, the next step involves inserting the Landsat bands previously downloaded from the Geobase website.

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 <br>
-The orthorectified GeoTiffs obtained from Geobase were already in an appropriate format and projection for our application therefore no image pre-processing was needed. For the purpose of this tutorial, the following bands were downloaded:
+The orthorectified GeoTIFFs obtained from Geobase were already in an appropriate format and projection for our application therefore no image pre-processing was needed. For the purpose of this tutorial, the following bands were downloaded:
 * Band 1 (Blue-green), 30 metres
 * Band 2 (Green), 30 metres

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 ===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.
+The first step in this tutorial is to find and download the free imagery 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% cloud, 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.
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 * After you are satisfied with your classes and training areas, click on Learn to start the classification. By clicking on Display, you can view the classification result in the viewer. The result will be added to the GUI and can now be saved.
 * The final step in the classification involves the validation. By clicking on the Validation button, the algorithm will create a Confusion Matrix and Accuracy result based on your classified image and random validation set.
+<br>
 ===Quantum GIS===

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 * Open the File menu and select '''Concatenate Images'''.
 * Add all the bands to the new image by selecting them individually and clicking the plus symbol. Once complete, you may modify the label name of the new image. As seen in Figure 2, all bands should be included.
-::[[image:concatenate.png|thumb|none|300px|Figure 2. Concatenate Menu]]
+::[[image:concatenate.png|thumb|none|400px|Figure 2. Concatenate Menu]]
 * Once the all the bands have been concatenated, a new folder named Ottawa is added to the main GUI which contains all the bands.
 * To display the bands in RGB, right click on the RGB folder (i.e. OutputImage) and select Display in Viewer.
 * Two windows should open; one will display the image while the other contains the image setup. In order to properly view the RGB image, make sure the RGB composition mode is selected in the image setup window and the appropriate RGB bands are inserted in the Red, Green, and Blue Channels. See Figure 3  for an example of the RGB setup.
-::[[image:rgb.png|thumb|none|300px|Figure 3. RGB Composition Mode]]
+::[[image:rgb.png|thumb|none|400px|Figure 3. RGB Composition Mode]]
 * By selecting on the Histogram tab in the image setup window, you have the option to modify the scaling parameters of the image therefore visually modifying the image to enhance natural color viewing capabilities. Having both the viewer window and setup window side by side is suggested in order to facilitate processing.
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 * At this stage, it is suggested to export your RGB file comprised of all 5 bands. To do so, right click on the RGB Ottawa folder and select Export Dataset. Navigate to an appropriate folder and give the RGB file a name with its appropriate extension (i.e. ottawa.tif). Change the data type to 8bit unsigned in order to limit the size of the file.
 <br>
 ====Create your Area of Interest====

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 * Select your newly created concatenated file. Cache the Image in order to increase processing time.
 * In the Select the ROI dialog box, create a box covering your AOI by dragging the cursor over the image. When satisfied with your AOI extent, click OK. Figure 4 contains an exmaple of the ROI dialog box
-::[[image:aoi.png|thumb|none|300px|Figure 4. Region of Interest Dialog Box]]
+::[[image:aoi.png|thumb|none|400px|Figure 4. Region of Interest Dialog Box]]
 * Save your AOI image by exporting the dataset as was done in the previous step.
 <br>
 ====Clustering====

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 * On the main title bar, select the Filtering menu>'''Mean Shift Clustering''' and select your AOI layer.
 * In the mean shift module dialog box, several parameters may be adjusted in order to obtain the desired image. For this purpose of this tutorial and considering we are using a medium resolution image, you will only increase the Min region size to 150. By checking the Display Boundaries and Display Cluster, you can view the boundaries and cluster generated by the algorithm. The opacity option allows you to adjust the transparency in order to visualize the original image along with the boundaries as seen in Figure 5. Before closing the Mean Shift module, run the algorithm. The Mean Shift algorithm will create 2 images, a clustered and filtered image; both are added to your GUI.
-::[[image:clustering.png|thumb|none|300px|Figure 5. Mean Shift Dialog Box]]
+::[[image:clustering.png|thumb|none|400px|Figure 5. Mean Shift Dialog Box]]
 * Save the clustered image by following the export procedures demonstrated earlier. We will use the clustered image for the classification.
 <br>
 ====Image Classification====