During the summer of 2015, I interned at BISAG. I, along with my friends Shantanu Shrivastava and Dharmil Shah worked on a project "Object Based Classification of Satellite Images"
We researched and worked on identifying individual objects like cars, aircraft, roads, etc from a high resolution satellite image. Rather than using the conventional pixel based approach for image classification, we used context sensitive object based approach for image classification.
I did this project as a part of my internship under the mentor-ship of Dr. Manoj Pandya and Dr. M. B. Poddar.
The major topics involved in the project include
Now another major question that arises is the need of object based classification rather than pixel based classification.
We can divide the complete process into 5 major steps
Errors may be introduced by
Below are the original image, image at a multi-resolution scale of 5 and image at a multi-resolution scale of 40
These are used for feature extraction.
Using decision trees and trained learners, we achieved the below classification results
For 2 class classification: Classified the original image into two classes, aircraft and ground
For 4 class classification: Classified the original image into four classes, aircraft, ground, shadow and road
Statistics for any classified image can be obtained by creating a mask image for a particular colour and extracting that coloured segment from the image and hence the number of pixels in the resulting image will give the approximate percentage of the colour in the original classified image.
The accuracy of our training model can be computed by