• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 15, Pages: 1527-1534

Original Article

An Innovative Runway Landing Path Detection using UAV Implementation of the K-Means Clustering Algorithm

Received Date:14 December 2023, Accepted Date:12 March 2024, Published Date:04 April 2024


Objective: To provide a novel approach for automatic Unmanned Aerial Vehicle (UAV) runway detection, leveraging remote sensing data and advanced image processing techniques. Methods: The methodology encompasses Gaussian filter-based despeckling and histogram equalization for preprocessing, followed by Independent Component Analysis (ICA) for feature extraction and segmentation using the K-means clustering algorithm. Findings: The research demonstrates successful UAV runway detection, even with unlabeled datasets, underscoring the efficacy of the proposed methods. Notably, the study contributes to automatic target recognition, specifically in Synthetic Aperture Radar (SAR) data analysis, where K-means clustering outperforms Korn B and morphological algorithms. Novelty : The K-means algorithms works by clustering the datasets obtained by integrating all the data collected from various sensors that are placed at specific positions in the runway. This work holds significance in facilitating immediate runway identification during emergencies and finds applications in military operations, surveillance, and remote sensing domains.

Keywords: Runway detection, Unmanned Aerial Vehicle, Histogram Equalization, Gaussian filtering, Independent Component Analysis, K-means clustering based segmentation


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@2024 Nagarajan & Jothiraj. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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