In this paper, we compare the sensitivity of two imaging configurations, both based on laser optical feedback imaging (LOFI). The first one is direct imaging, which uses conventional optical focalization on target, and the second one is made by a synthetic aperture (SA) laser, which uses numerical focalization. We show that SA configuration allows us to obtain good resolutions with high working distance and that the drawback of SA imagery is that it has a worse photometric balance in comparison to a conventional microscope. This drawback is partially compensated by the important sensitivity of LOFI. Another interest of SA relies on the capacity of getting three-dimensional information in a single x-y scan.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/JOSAA.29.000476 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Soil moisture is a key parameter for the exchange of substance and energy at the land-air interface, timely and accurate acquisition of soil moisture is of great significance for drought monitoring, water resource management, and crop yield estimation. Synthetic aperture radar (SAR) is sensitive to soil moisture, but the effects of vegetation on SAR signals poses challenges for soil moisture retrieval in areas covered with vegetation. In this study, based on Sentinel-1 SAR and Sentinel-2 optical remote sensing data, a coupling approach was employed to retrieval surface soil moisture over dense vegetated areas.
View Article and Find Full Text PDFOptical diffraction tomography using a self-reference module.
Biomed Opt Express
January 2025
Department of Electronic Engineering, Maynooth University, Maynooth, Co. Kildare, Ireland.
Optical diffraction tomography enables label-free, 3D refractive index (RI) imaging of biological samples. We present a novel, cost-effective approach to ODT that employs a modular design incorporating a self-reference holographic capture module. This two-part system consists of an illumination module and a capture module that can be seamlessly integrated with any life-science microscope using an automated alignment protocol.
View Article and Find Full Text PDFHongtang Bridge Expansion Joints InSAR Deformation Monitoring with Advanced Phase Unwrapping and Mixed Total Least Squares in Fuzhou China.
Sensors (Basel)
December 2024
College of Civil Engineering, Xiangtan University, Xiangtan 411105, China.
Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR).
View Article and Find Full Text PDFCharacterizing Surface Deformation of the Earthquake-Induced Daguangbao Landslide by Combining Satellite- and Ground-Based InSAR.
Sensors (Basel)
December 2024
School of Environment and Resources, Southwest University of Science and Technology, Mianyang 621010, China.
The Daguangbao landslide (DGBL), triggered by the 2008 Wenchuan earthquake, is a rare instance of super-giant landslides globally. The post-earthquake evolution of the DGBL has garnered significant attention in recent years; however, its deformation patterns remain poorly characterized owing to the complex local topography. In this study, we present the first observations of the surface dynamics of DGBL by integrating satellite- and ground-based InSAR data complemented by kinematic interpretation using a LiDAR-derived Digital Surface Model (DSM).
View Article and Find Full Text PDFA Combined CNN-LSTM Network for Ship Classification on SAR Images.
Sensors (Basel)
December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!