SatelliteImageClassification

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
Ensemble Classifier: Water vs Everything, without filtering	Ensemble Classifier: Water vs Everything, after mode filtering

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.