Polarimetric SAR Data Denoising using SOFM

• Author(s): Amit Kumar Pandey ; Mithilesh Vishwakarma ; Bipin Yadav ; Gopal Rajbhar
• Paper ID: 1705252
• Page: 42-48
• Published Date: 05-12-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 6 December-2023

Abstract

Synthetic Aperture Radar (SAR) data plays a critical role in remote sensing applications, providing valuable information for various fields, including environmental monitoring, disaster management, and defense. However, SAR data is often contaminated by noise, which can degrade the quality of the information extracted from it. In this research, we address the problem of denoising polarimetric SAR data using Self-Organizing Feature Maps (SOFM), a neural network-based approach. The motivation behind this work stems from the necessity to enhance the quality and accuracy of SAR data for improved interpretation and analysis. We propose a methodology that leverages the unsupervised learning capabilities of SOFM to reduce noise in polarimetric SAR images. The key idea is to find the best-matching unit (BMU) for each pixel in the data and use it to update the noisy pixel with information from its corresponding BMU. In the literature review, we discuss the significance of SAR data and review existing denoising techniques, highlighting the advantages and limitations of each. We present the design strategy, including the choice of SOFM as the denoising tool and the selection of parameters. The methodology section offers a detailed description of the denoising process, emphasizing the calculation of BMUs and the update of noisy data based on these BMUs. We also provide insights into the design of the SOFM network and its training process. While partial results from the validation process are presented, the project remains a work in progress, with ongoing experiments and further analysis. Our approach shows promise in improving the quality of polarimetric SAR data, but comprehensive validation and fine-tuning are required to assess its full potential. In conclusion, this project aims to contribute to the advancement of SAR data processing by introducing a novel denoising technique based on Self-Organizing Feature Maps. By reducing noise in polarimetric SAR data, our methodology has the potential to enhance the accuracy and reliability of information derived from SAR images, thus benefiting a wide range of applications in remote sensing.

Keywords

Polarimetric SAR Data, Denoising, Self-Organizing Feature Maps (SOFM), Remote Sensing, Synthetic Aperture Radar (SAR), Noise Reduction

Citations

IRE Journals:
Amit Kumar Pandey , Mithilesh Vishwakarma , Bipin Yadav , Gopal Rajbhar "Polarimetric SAR Data Denoising using SOFM" Iconic Research And Engineering Journals Volume 7 Issue 6 2023 Page 42-48

IEEE:
Amit Kumar Pandey , Mithilesh Vishwakarma , Bipin Yadav , Gopal Rajbhar "Polarimetric SAR Data Denoising using SOFM" Iconic Research And Engineering Journals, 7(6)