Thermal Noise Removal From Polarimetric Sentinel-1 Data
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/112608
Título: | Thermal Noise Removal From Polarimetric Sentinel-1 Data |
---|---|
Autor/es: | Mascolo, Lucio | Lopez-Sanchez, Juan M. | Cloude, Shane R. |
Grupo/s de investigación o GITE: | Señales, Sistemas y Telecomunicación |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Polarimetry | Sentinel-1 | Thermal noise |
Área/s de conocimiento: | Teoría de la Señal y Comunicaciones |
Fecha de publicación: | 21-ene-2021 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Geoscience and Remote Sensing Letters. 2022, 19: 4009105. https://doi.org/10.1109/LGRS.2021.3050921 |
Resumen: | This study proposes, for the first time, an approach to remove thermal noise from the wave coherency matrix, C₂, estimated from single-look complex dual-polarization Interferometric Wide Swath mode Sentinel-1 synthetic aperture radar data. The approach is straightforward; it exploits the ThermalNoiseRemoval module, provided by the European Space Agency (ESA) in its Sentinel Application Platform (SNAP) software, to remove thermal noise from the channel intensities. Then, noise correction on the complex data is applied, in order to estimate the noise-free C₂ matrix. As a further novelty, the proposed approach can be implemented in SNAP, through the use of a processing graph that is here provided. The method is applied on a dense time series of Sentinel-1 data, collected on an agricultural area located near Seville, Spain. The impact of thermal noise on the estimation of the eigendecomposition parameters of C₂, i.e., entropy (H₂), average alpha angle (α₂), and anisotropy (A₂), is assessed for different land-cover types, namely river, rice, forest, and urban areas. Monte Carlo simulations are implemented to assess the performance of the proposed approach in estimating H₂, α₂, and A₂. Results show that the proposed noise removal method improves the estimation of these parameters for the considered land-cover classes. |
URI: | http://hdl.handle.net/10045/112608 |
ISSN: | 1545-598X (Print) | 1558-0571 (Online) |
DOI: | 10.1109/LGRS.2021.3050921 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/LGRS.2021.3050921 |
Aparece en las colecciones: | INV - SST - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Mascolo_etal_2021_IEEE-GRSL_accepted.pdf | Accepted Manuscript (acceso abierto) | 2,23 MB | Adobe PDF | Abrir Vista previa |
Mascolo_etal_2021_IEEE-GRSL_final.pdf | Versión final (acceso restringido) | 2,1 MB | Adobe PDF | Abrir Solicitar una copia |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.