Supervised classification for object identification in urban areas using satellite imagery
This is an incremental improvement for remote sensing applications in urban planning.
The paper tackles object identification in urban areas using satellite imagery by comparing Support Vector Machine (SVM) and Naive Bayes classifiers with textural features, resulting in Naive Bayes achieving 76% accuracy versus 68% for SVM and computational times of 27-45 seconds per image.
This paper presents a useful method to achieve classification in satellite imagery. The approach is based on pixel level study employing various features such as correlation, homogeneity, energy and contrast. In this study gray-scale images are used for training the classification model. For supervised classification, two classification techniques are employed namely the Support Vector Machine (SVM) and the Naive Bayes. With textural features used for gray-scale images, Naive Bayes performs better with an overall accuracy of 76% compared to 68% achieved by SVM. The computational time is evaluated while performing the experiment with two different window sizes i.e., 50x50 and 70x70. The required computational time on a single image is found to be 27 seconds for a window size of 70x70 and 45 seconds for a window size of 50x50.