• Version
• Download 13
• File Size 477.45 KB
• File Count 1
• Create Date 23 June 2024
• Last Updated 23 June 2024

Enhancing Remote Sensing Data Analysis with Machine Learning Techniques

1Nazeer Shaik, 2Abdul Subhahan Shaik, 3Dr.C. Krishna Priya

1Dept of CSE, Srinivasa Ramanujan Institute of Technology (Autonomous), Anantapur.

2Dept of CSE, Crimson Institute of Technology, Hyderabad.

3Dept. of Computer Science & IT, Central University of Andhra Pradesh, Anantapur.

Abstract

The integration of machine learning techniques in remote sensing data analysis has significantly advanced the field, enabling more accurate, efficient, and scalable analysis of vast datasets. This paper explores the enhancement of remote sensing data analysis through the application of various machine learning algorithms. It reviews related works, highlighting the evolution and current state of machine learning in remote sensing. The existing systems are critically examined to identify their limitations, such as handling high-dimensional data and scalability issues. To address these limitations, this paper proposes a novel deep learning-based network tailored for remote sensing applications. The proposed system leverages convolutional neural networks (CNNs) for feature extraction and classification, and recurrent neural networks (RNNs) for temporal data analysis. Additionally, we integrate generative adversarial networks (GANs) for data augmentation to improve model robustness and performance. Experimental results demonstrate the superiority of the proposed system over existing methods in terms of accuracy, efficiency, and scalability. A comparative analysis with traditional machine learning models and recent deep learning architectures is provided, showcasing significant improvements in key performance metrics. The discussion delves into the implications of these findings for real-world applications, including land cover classification, change detection, and disaster management. Future enhancements are proposed to further refine the system, such as incorporating more diverse data sources and improving computational efficiency. In conclusion, this paper demonstrates the transformative potential of advanced machine learning techniques in remote sensing data analysis, paving the way for more precise and insightful environmental monitoring and decision-making.

Keywords: Remote sensing, machine learning, deep learning, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), data augmentation, feature extraction, classification, environmental monitoring.