1,852 research outputs found
Developing a remote sensing system based on X-band radar technology for coastal morphodynamics study
New data processing techniques are proposed for the assessment of scopes and limitations from radar-derived sea state parameters, coastline evolution and water depth estimates. Most of the raised research is focused on Colombian Caribbean coast and the Western Mediterranean Sea. First, a novel procedure to mitigate shadowing in radar images is proposed. The method compensates distortions introduced by the radar acquisition process and the power decay of the radar signal along range applying image enhancement techniques through a couple of pre-processing steps based on filtering and interpolation. Results reveal that the proposed methodology reproduces with high accuracy the sea state parameters in nearshore areas. The improvement resulting from the proposed method is assessed in a coral reef barrier, introducing a completely novel use for X-Band radar in coastal environments. So far, wave energy dissipation on a coral reef barrier has been studied by a few in-situ sensors placed in a straight line, perpendicular to the coastline, but never been described using marine radars. In this context, marine radar images are used to describe prominent features of coral reefs, including the delineation of reef morphological structure, wave energy dissipation and wave transformation processes in the lagoon of San Andres Island barrier-reef system. Results show that reef attenuates incident waves by approximately 75% due to both frictional and wave breaking dissipation, with an equivalent bottom roughness of 0.20 m and a wave friction factor of 0.18. These parameters are comparable with estimates reported in other shallow coral reef lagoons as well as at meadow canopies, obtained using in-situ measurements of wave parameters.DoctoradoDoctor en IngenierÃa Eléctrica y Electrónic
Non-Local Compressive Sensing Based SAR Tomography
Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse
reconstruction problem and, hence, can be solved using compressive sensing (CS)
algorithms. This paper proposes solutions for two notorious problems in this
field: 1) TomoSAR requires a high number of data sets, which makes the
technique expensive. However, it can be shown that the number of acquisitions
and the signal-to-noise ratio (SNR) can be traded off against each other,
because it is asymptotically only the product of the number of acquisitions and
SNR that determines the reconstruction quality. We propose to increase SNR by
integrating non-local estimation into the inversion and show that a reasonable
reconstruction of buildings from only seven interferograms is feasible. 2)
CS-based inversion is computationally expensive and therefore barely suitable
for large-scale applications. We introduce a new fast and accurate algorithm
for solving the non-local L1-L2-minimization problem, central to CS-based
reconstruction algorithms. The applicability of the algorithm is demonstrated
using simulated data and TerraSAR-X high-resolution spotlight images over an
area in Munich, Germany.Comment: 10 page
Measurement of sea waves
Sea waves constitute a natural phenomenon with a great impact on human activities, and their monitoring is essential for meteorology, coastal safety, navigation, and renewable energy from the sea. Therefore, the main measurement techniques for their monitoring are here reviewed, including buoys, satellite observation, coastal radars, shipboard observation, and microseism analysis. For each technique, the measurement principle is briefly recalled, the degree of development is outlined, and trends are prospected. The complementarity of such techniques is also highlighted, and the need for further integration in local and global networks is stressed
 Ocean Remote Sensing with Synthetic Aperture Radar
The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography
Measurement of Sea Waves
Sea waves constitute a natural phenomenon with a great impact on human activities, and their monitoring is essential for meteorology, coastal safety, navigation, and renewable energy from the sea. Therefore, the main measurement techniques for their monitoring are here reviewed, including buoys, satellite observation, coastal radars, shipboard observation, and microseism analysis. For each technique, the measurement principle is briefly recalled, the degree of development is outlined, and trends are prospected. The complementarity of such techniques is also highlighted, and the need for further integration in local and global networks is stressed
Evaluation and improvement of methods for estimating sea surface wave parameters from X-band marine radar data
In this thesis, several algorithms have been proposed for estimating ocean wave parameters
from X-band marine radar data, i.e., wave direction, wave period, and significant wave
height. In the first part of this study, the accuracy of wave direction and period estimation
from X-band marine radar images under different rain rates is analyzed, and a sub-image
selection scheme is proposed to mitigate the rain effect. Firstly, each radar image is divided
into multiple sub-images, and the sub-images with relatively clear wave signatures are
identified based on a random-forest based classiffication model. Then, wave direction is estimated
by performing a Radon transform (RT) on each valid sub-image. As for wave period
estimation, a random-forest based regression method is proposed. Texture features are first
extracted from each pixel of the selected sub-image using the gray-level co-occurrence matrix
(GLCM) and combined as a feature vector. Those feature vectors extracted from both
rain-free and rain-contaminated training samples are then used to train a random-forest
based wave period regression model. Shore-based X-band marine radar images, simultaneous
rain rate data, as well as buoy-measured wave data collected on the West Coast of the
United States are used to analyze the rain effect on wave parameter estimation accuracy
and to validate the proposed method. Experimental results show that the proposed subimage
selection scheme improves the estimation accuracy of both wave direction and wave
period under different rain rates, with reductions of root-mean-square errors (RMSEs) by
6.9゚, 6.0゚, 4.9゚, and 1.0゚ for wave direction under rainless, light rain, moderate rain, and
heavy rain conditions, respectively. As for wave period estimation, the RMSEs decrease by
0.13 s, 0.20 s, 0.30 s, and 0.20 s under those four rainfall intensity levels, respectively.
The second part of research focuses on the estimation of significant wave height (Hâ‚›).
A temporal convolutional network (TCN)-based model is proposed to retrieve Hâ‚› from X-band
marine radar image sequences. Three types of features are first extracted from radar
image sequences based on signal to noise ratio (SNR), ensemble empirical mode decomposition
(EEMD), and GLCM methods, respectively. Then, feature vectors are input into the
proposed TCN-based regression model to produce Hâ‚› estimation. Radar data are collected
from a moving vessel at the East Coast of Canada, as well as simultaneously collected wave
data from several wave buoys deployed nearby are used for model training and testing. After
averaging, experimental results show that the TCN-based model further improves the Hâ‚› estimation
accuracy, with reductions of RMSEs by 0.33 m and 0.10 m, respectively, compared
to the SNR-based and the EEMD-based linear fitting methods. It has also been found that
with the same feature extraction scheme, TCN outperforms other machine-learning based
algorithms including support vector regression (SVR) and the convolutional gated recurrent
unit (CGRU) network
Wave Measurements
Purpose of the present report is the summary of the experimental campaign performed at INSEAN facilities. This campaign has been oriented to analyze the classical wave measurement systems and, furthermore, to validate the results of the numerical models. A devoted paragraph describes the main features of a new innovative and non intrusive methodology for the wave measurements aimed to perform both model and ship scale trials
Summaries of the Sixth Annual JPL Airborne Earth Science Workshop
The Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996, was divided into two smaller workshops:(1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, and The Airborne Synthetic Aperture Radar (AIRSAR) workshop. This current paper, Volume 2 of the Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, presents the summaries for The Airborne Synthetic Aperture Radar (AIRSAR) workshop
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