Supervised classification for object identification in urban areas using satellite imagery

TL;DR

This study compares supervised classification techniques, SVM and Naive Bayes, for object identification in satellite imagery, finding Naive Bayes more accurate with reasonable computational efficiency.

Contribution

It introduces a pixel-level classification method using textural features and evaluates two supervised classifiers on gray-scale satellite images.

Findings

Naive Bayes achieves 76% accuracy, outperforming SVM's 68%.

Larger window size reduces computational time.

Naive Bayes is more suitable for this classification task.

Abstract

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.