Target Detection in Satellite Images using Deep Learning and YOLO Algorithm : An Implementation

Supriya Suresh Pohekar , Mr.Mayur Tiwari , Dr. S.M. Deshmukh

Anomaly detection, Unsupervised Anomaly Detection, Semi-supervised Anomaly Detection, Supervised Anomaly Detection, deep learning

Anomaly detection in satellite images plays a pivotal role in various fields such as environmental monitoring, urban planning, and agriculture. Traditional methods for anomaly detection often face challenges in effectively capturing complex patterns and anomalies within large-scale satellite imagery datasets. This research paper proposes a novel approach utilizing deep learning techniques, specifically You Only Look Once (YOLO), for anomaly detection in satellite images. The YOLO framework offers real-time object detection capabilities and is adapted to identify anomalies with high precision and recall. We present a comprehensive methodology for training and evaluating the YOLO model using a diverse dataset of satellite images. Experimental results demonstrate the effectiveness of the proposed approach in detecting anomalies across different scenarios, achieving competitive performance metrics compared to baseline methods. Furthermore, qualitative analysis showcases the ability of the model to accurately localize and classify various types of anomalies within satellite imagery. This research contributes to advancing the field of anomaly detection in satellite imagery, offering a robust and efficient solution with practical implications for remote sensing applications.

Published Paper   E-Certificate

"Target Detection in Satellite Images using Deep Learning and YOLO Algorithm : An Implementation", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 5, page no.571 - 576, May-2024, Available :https://ijsdr.org/papers/IJSDR2405080.pdf

Volume 9 Issue 5, May-2024

Pages : 571 - 576

Paper Reg. ID: IJSDR_211173

Published Paper Id: IJSDR2405080

Downloads: 000347282

Research Area: Electronics & Communication Engg. 

Country: Amravati, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2405080

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2405080

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition