This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/tip.2005.851690 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
GSCAT-UNET: Enhanced U-Net model with spatial-channel attention gate and three-level attention for oil spill detection using SAR data.
Mar Pollut Bull
January 2025
JK Laxmipat University, Jaipur, Rajasthan, India.
Marine pollution due to oil spills presents major risks to coastal areas and aquatic life, leading to serious environmental health concerns. Oil Spill detection using SAR data has transitioned from traditional segmentation to a variety of machine learning & deep learning models like UNET proving its efficiency for the task. This research paper proposes a GSCAT-UNET model for efficient oil spill detection and discrimination from lookalikes.
View Article and Find Full Text PDFTime-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm.
Sensors (Basel)
January 2025
Satellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of Korea.
For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations.
View Article and Find Full Text PDFDual-Modal Approach for Ship Detection: Fusing Synthetic Aperture Radar and Optical Satellite Imagery.
Sensors (Basel)
January 2025
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
The fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light conditions, reducing its reliability for maritime applications. In contrast, SAR imagery excels in these scenarios but is prone to noise and clutter, complicating vessel detection.
View Article and Find Full Text PDFEfficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation.
J Imaging
January 2025
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213000, China.
Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential medical resources and impedes the advancement of computer-aided diagnosis. This paper introduces a novel medical image-segmentation framework named Efficient Generative-Adversarial U-Net (EGAUNet), designed to facilitate rapid and accurate multi-organ labeling. To enhance the model's capability to comprehend spatial information, we propose the Global Spatial-Channel Attention Mechanism (GSCA).
View Article and Find Full Text PDFRat models of postintracerebral hemorrhage pneumonia induced by nasal inoculation with or intratracheal inoculation with LPS.
Front Immunol
January 2025
State Key Laboratory of Traditional Chinese Medicine Syndrome, Department of Neurology, Guangdong Provincial Academy of Chinese Medical Sciences, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Background: A stable and reproducible experimental bacterial pneumonia model postintracerebral hemorrhage (ICH) is necessary to help investigating the pathogenesis and novel treatments of Stroke-associated pneumonia (SAP).
Aim: To establish a Gram-negative bacterial pneumonia-complicating ICH rat model and an acute lung injury (ALI)-complicating ICH rat model.
Methods: We established two standardized models of post-ICH pneumonia by nasal inoculation with () or intratracheal inoculation with lipopolysaccharide (LPS).
Want 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!