@inproceedings{a3f4fcc6d2a4447a98d8c6817ebe2e86,
title = "Supervised classification for object identification in urban areas using satellite imagery",
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 Na{\"i}ve Bayes. With textural features used for gray-scale images, Na{\"i}ve 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., 50 × 50 and 70 × 70. The required computational time on a single image is found to be 27 seconds for a window size of 70 × 70 and 45 seconds for a window size of 50 × 50.",
keywords = "Classification, Na{\"i}ve Bayes, SVM, Satellite Imagery",
author = "Hazrat Ali and Awan, {Adnan Ali} and Sanaullah Khan and Omer Shafique and {Ur Rahman}, Atiq and Shahid Khan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2018 ; Conference date: 03-03-2018 Through 04-03-2018",
year = "2018",
month = apr,
day = "24",
doi = "10.1109/ICOMET.2018.8346383",
language = "English",
series = "2018 International Conference on Computing, Mathematics and Engineering Technologies: Invent, Innovate and Integrate for Socioeconomic Development, iCoMET 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "2018 International Conference on Computing, Mathematics and Engineering Technologies",
address = "United States",
}