6,980 research outputs found
Automatic and semi-automatic extraction of curvilinear features from SAR images
Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images
Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico
Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platformsĂąâŹâą backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation
Radar shadow detection in SAR images using DEM and projections
Synthetic aperture radar (SAR) images are widely used in target recognition
tasks nowadays. In this letter, we propose an automatic approach for radar
shadow detection and extraction from SAR images utilizing geometric projections
along with the digital elevation model (DEM) which corresponds to the given
geo-referenced SAR image. First, the DEM is rotated into the radar geometry so
that each row would match that of a radar line of sight. Next, we extract the
shadow regions by processing row by row until the image is covered fully. We
test the proposed shadow detection approach on different DEMs and a simulated
1D signals and 2D hills and volleys modeled by various variance based Gaussian
functions. Experimental results indicate the proposed algorithm produces good
results in detecting shadows in SAR images with high resolution.Comment: 10 pages, 6 figure
Estimation of the Degree of Polarization for Hybrid/Compact and Linear Dual-Pol SAR Intensity Images: Principles and Applications
Analysis and comparison of linear and hybrid/compact dual-polarization (dual-pol) synthetic aperture radar (SAR) imagery have gained a wholly new importance in the last few years, in particular, with the advent of new spaceborne SARs such as the Japanese ALOS PALSAR, the Canadian RADARSAT-2, and the German TerraSAR-X. Compact polarimetry,
hybrid dual-pol, and quad-pol modes are newly promoted in the literature for future SAR missions. In this paper, we investigate and compare different hybrid/compact and linear dual-pol modes in terms of the estimation of the degree of polarization (DoP). The DoP has long been recognized as one of the most important parameters characterizing a partially polarized electromagnetic wave. It can be effectively used to characterize the information content of SAR data. We study and compare the information content of the intensity data provided by different hybrid/compact and linear dual-pol SAR modes. For this purpose, we derive the joint distribution of multilook SAR intensity images. We use this
distribution to derive the maximum likelihood and moment-based estimators of the DoP in hybrid/compact and linear dual-pol modes.We evaluate and compare the performance of these estimators for different modes on both synthetic and real data, which are acquired by RADARSAT-2 spaceborne and NASA/JPL airborne SAR systems, over various terrain types such as urban, vegetation, and ocean
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information
drawn from co-registered optical imagery. Filtering is performed by plain
patch-wise nonlocal means, operating exclusively on SAR data. However, the
filtering weights are computed by taking into account also the optical guide,
which is much cleaner than the SAR data, and hence more discriminative. To
avoid injecting optical-domain information into the filtered image, a
SAR-domain statistical test is preliminarily performed to reject right away any
risky predictor. Experiments on two SAR-optical datasets prove the proposed
method to suppress very effectively the speckle, preserving structural details,
and without introducing visible filtering artifacts. Overall, the proposed
method compares favourably with all state-of-the-art despeckling filters, and
also with our own previous optical-guided filter
Ship and Oil-Spill Detection Using the Degree of Polarization in Linear and Hybrid/Compact Dual-Pol SAR
Monitoring and detection of ships and oil spills using
synthetic aperture radar (SAR) have received a considerable attention over the past few years, notably due to the wide area coverage and day and night all-weather capabilities of SAR systems. Among different polarimetric SAR modes, dual-pol SAR data are widely used for monitoring large ocean and coastal areas. The degree of polarization (DoP) is a fundamental quantity characterizing a partially polarized electromagnetic field, with significantly less computational complexity, readily adaptable for on-board implementation, compared with other well-known polarimetric discriminators. The performance of the DoP is studied for joint ship and oil-spill detection under different polarizations in hybrid/compact and linear dual-pol SAR imagery. Experiments are performed on RADARSAT-2 -band polarimetric data sets, over San Francisco Bay, and -band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico
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