Abstract: This paper studies the different unsupervised segmentation algorithms that have been proposed and their efficacy on thermal images. The scope of this research is to develop a generalized approach to blindly segment urban thermal imagery to assist the system in identifying regions by shape instead of pixel values. Most methods can be classified as thresholding, edge-based, region-based, clustering, or texture analysis. We explained methods, worked before applying the methods of interest on thermal images of 8-bit and 16-bit resolution, and evaluated the performance. The evaluation section discusses where each method succeeded, where it failed, and how the performance can be enhanced. Finally, we study the time complexity of each method to assess the feasibility of implementing a fast, and generalized method of pixel labeling.

Mohammed Abuhussein, Aaron L. Robinson and Iyad Almadani, “Review of Unsupervised Segmentation Techniques on Long Wave Infrared Images” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01406138

@article{Abuhussein2023,
title = {Review of Unsupervised Segmentation Techniques on Long Wave Infrared Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01406138},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01406138},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Mohammed Abuhussein and Aaron L. Robinson and Iyad Almadani}
}