For moving targets in synthetic aperture radar (SAR) images, the obvious features are defocusing and dislocation. To estimate motion parameters accurately is a premise for the precise imaging of moving targets. Howeve...
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ISBN:
(纸本)9781665468893
For moving targets in synthetic aperture radar (SAR) images, the obvious features are defocusing and dislocation. To estimate motion parameters accurately is a premise for the precise imaging of moving targets. However, when the radial velocity of the target exceeds the maximum detectable unambiguous velocity, the estimated value by the existing methods is no longer the real value. A radial velocity ambiguity resolution method based on Imaging Space-Time Adaptive processing (ISTAP) is proposed in this paper. The proposed method has no search or iterative process and is suitable for the azimuth multichannel SAR with low pulse repetition frequency (PRF). Finally, the simulated data from an x-band azimuth six-channel SAR system verify the feasibility of the proposed method.
This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single mea...
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Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularizati...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L
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norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.
Road extraction from high-resolution remote sensing images has been applied in many domains, but it is still full of challenges. We focus on the problem of slender roads, proposing a new multiple feature pyramid netwo...
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Road extraction from high-resolution remote sensing images has been applied in many domains, but it is still full of challenges. We focus on the problem of slender roads, proposing a new multiple feature pyramid network (MFPN), which is composed of an effective feature pyramid and the tailored pyramid pooling module based on PSPNet. These two designs can address the sparsity of roads in remote sensing images via using multi-level semantic features. Experiments on remote sensing images from Quick Bird show that our MFPN model achieves competitive performance, especially for slender roads.
Highly-squinted synthetic aperture radar (SAR) echo has the characteristic of severe range-azimuth coupling, requiring specialized imaging algorithms. applications of compressed sensing in SAR imaging can effectively ...
Highly-squinted synthetic aperture radar (SAR) echo has the characteristic of severe range-azimuth coupling, requiring specialized imaging algorithms. applications of compressed sensing in SAR imaging can effectively improve the resolution and other indicators. However, inaccurate manual parameters can affect the algorithm output. This article proposes an improved alternating direction method of multipliers (ADMM) for solving sparse reconstruction models under highly-squinted conditions. By adaptively adjusting the penalty parameter in ADMM via hyper-gradient descent (HD), the problem caused by inaccurate manual parameter is solved. Compared with matched filtering methods and other optimization methods, this method can suppress noise and speed up convergence. The effectiveness of the proposed method can be validated through the approximate observation of both simulated scenes and real scenes captured by the GF-3 SAR satellite.
With the SAR satellites have gradually become one of the most important methods of Earth observation, rapid interpretation of SAR images has become particularly important. However, the unique imaging mechanism of SAR ...
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—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, met...
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Through the recent performance of convolutional neural networks in image processing tasks, we propose a deep fully convolutional network for remote sensing image inpainting. The proposed Dense-Add Net (Dense-Add Netwo...
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Through the recent performance of convolutional neural networks in image processing tasks, we propose a deep fully convolutional network for remote sensing image inpainting. The proposed Dense-Add Net (Dense-Add Network) can alleviate the vanishing-gradient problem, strengthen feature reuse, and substantially reduce the memory usage. We apply residual learning to learn the mappings from corrupted image to recovered image directly;it will back-propagate gradient to the bottom layers and accelerate the training process. We train the proposed Dense-Add Net with a robust Charbonnier loss function which can achieve high-quality reconstruction. The experimental verify the efficacy of our proposed Dense-Add Net.
Tomographic SAR (TomoSAR) technology has gained significant attention in recent years due to its three-dimensional imaging capability. However, in practical applications, phase errors between different channels can de...
Tomographic SAR (TomoSAR) technology has gained significant attention in recent years due to its three-dimensional imaging capability. However, in practical applications, phase errors between different channels can degrade the quality of three-dimensional imaging. Current state-of-the-art methods for phase error compensation based on autofocus techniques suffer from high computational complexity, making them unsuitable for large-scale three-dimensional imaging. In this paper, we propose a multi-channel phase error estimation method based on error back-propagation training optimization. By utilizing the TomoSAR model that incorporates phase errors from multiple channels, we construct a matrix containing the parameters to be estimated for inter-channel phase errors. Through stochastic gradient descent algorithm, we iteratively optimize the parameters of the phase error matrix, ultimately obtaining an estimation of the inter-channel phase errors. Experimental results validate the accuracy of the proposed method.
A visually inspired variational method for automatic image registration is proposed to solve local deformation which traditional global registration model cannot well satisfy. The variational model considers local tra...
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