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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: June 2024

Volume 9 | Issue 6

Impact factor: 8.15

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Paper Title: Target Detection in Satellite Images using Deep Learning and YOLO Algorithm : An Implementation
Authors Name: Supriya Suresh Pohekar , Mr.Mayur Tiwari , Dr. S.M. Deshmukh
Unique Id: IJSDR2405080
Published In: Volume 9 Issue 5, May-2024
Abstract: 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.
Keywords: Anomaly detection, Unsupervised Anomaly Detection, Semi-supervised Anomaly Detection, Supervised Anomaly Detection, deep learning
Cite Article: "Target Detection in Satellite Images using Deep Learning and YOLO Algorithm : An Implementation", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 5, page no.571 - 576, May-2024, Available :http://www.ijsdr.org/papers/IJSDR2405080.pdf
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Publication Details: Published Paper ID: IJSDR2405080
Registration ID:211173
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 571 - 576
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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