Pixel based classification of satellite imagery
- sample training and testing points generated using Point Sampling plugin in QGIS
- feature generation using Orfeo Toolbox
- feature selection using Learning Vector Quantization
- CLassification using Decision Tree, Neural Networks, Random Forests, KNN and Naive Bayes Classifier
- Ensemble classifier for Flood Inundation Mapping - classifies a pixel as water if 2 or more than 2 of the above classifiers classify a pixel as water
- Mode filter used to remove individually wrongly classified pixels
- Classification accuracy to measure goodness of each model
Outcomes of the best classifier (Multi Layer Perceptron) are as shown below:
To compile and run SatelliteClassification.java, you need weka.jar that you can download from the Weka website.
Compile code: javac -cp weka.jar SatelliteClassification.java
Run code: java -cp weka.jar:. SatelliteClassification "trainingFile" "testingFile" "classifiername"
- order:
|clouds: white | |roads: yellow | |shadow: black | |urban: pink | |vegetation: green | |water: blue |
| Original LANDSAT 8 Image during Flooding | Multi Layer Perceptron Classification |
|---|---|
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| Ensemble Classifier: Water vs Everything, without filtering | Ensemble Classifier: Water vs Everything, after mode filtering |
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References:
- LANDSAT-8 imagery(http://earthexplorer.usgs.gov/)
- Image Preprocessing - QGIS (http://www.qgis.org/en/site/), ArcGIS (https://www.arcgis.com/features/index.html)
- Feature selection performed using Orfeo Toolbox (https://www.orfeo-toolbox.org/)
- Feature Selection: Vatsavai, Ranga Raju. "High-resolution urban image classification using extended features." In 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 869-876. IEEE, 2011.