Integrating Synthetic Aperture Radar (SAR) imaging with unmanned aerial vehicles (UAVs) plays a crucial role in urban area surveillance and situational awareness, benefiting from the low cost, small size, and high fle...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
Integrating Synthetic Aperture Radar (SAR) imaging with unmanned aerial vehicles (UAVs) plays a crucial role in urban area surveillance and situational awareness, benefiting from the low cost, small size, and high flexibility of SAR carried by drones. However, the dense arrangement of high-rise buildings in urban environments creates Urban Canyons and numerous visual blind spots due to occlusion, which weakens the perception capability of SAR. Additionally, SAR imaging results of moving vehicles on the roads between buildings result in severe defocusing due to their non-cooperative motion. In this paper, we establish a vehicle signal model for SAR imaging with UAVs that considers the vehicle body’s translation and the wheels’ rotation. The range history modulation and imaging characteristics of returns caused by translation and micro-motion are derived. Simulation results validate the correctness of the theoretical analysis, and the proposed theory helps explain SAR imaging results, providing support for high-precision focusing and three-dimensional imaging of non-line-of-sight (NLOS) SAR images.
SAR image understanding is a significant but also challenging issue in practice. In order to detect the difference between single-polarized and polarimetric SAR (PolSAR) data and explore more information from the pre ...
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Multi-Aspect SAR can obtain rich backscatter information about the illuminated scene. For different kinds of targets in the scene, scattering information related to the geometric characteristics of the target is shown...
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The circular synthetic aperture radar (SAR) can observe the experimental scene from all angles. The backscatter intensity of the target in the scene can be obtained. Different targets in the imaging scene show differe...
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We have witnessed the revolutionary progress of learned image compression despite a short history of this field. Some challenges still remain such as computational complexity that prevent the practical application of ...
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We have witnessed the revolutionary progress of learned image compression despite a short history of this field. Some challenges still remain such as computational complexity that prevent the practical application of learning-based codecs. In this paper, we address the issue of heavy time complexity from the view of arithmetic coding. Prevalent learning-based image compression scheme first maps the natural image into latent representations and then conduct arithmetic coding on quantized latent maps. Previous arithmetic coding schemes define the start and end value of the arithmetic codebook as the minimum and maximum of the whole latent maps, ignoring the fact that the value ranges in most channels are shorter. Hence, we propose to use a channel-adaptive codebook to accelerate arithmetic coding. We find that the latent channels have different frequency-related characteristics, which are verified by experiments of neural frequency filtering. Further, the value ranges of latent maps are different across channels which are relatively image-independent. The channel-adaptive characteristics allow us to establish efficient prior codebooks that cover more appropriate ranges to reduce the runtime. Experimental results demonstrate that both the arithmetic encoding and decoding can be accelerated while preserving the rate-distortion performance of compression model.
Carbonyl sulfide(COS)is an effective tracer for estimating Gross Primary Productivity(GPP)in the carbon *** the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately *** this study,an a...
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Carbonyl sulfide(COS)is an effective tracer for estimating Gross Primary Productivity(GPP)in the carbon *** the largest contribution to the atmosphere,anthropogenic COS emissions must be accurately *** this study,an anthropogenic COS emission inventory from 2015 to 2021 was constructed by applying the bottom-up approach based on activity data from emission ***’s anthropogenic COS emissions increased from approximately 171 to 198 Gg S yr^(-1)from 2015-2021,differing from the trends of other *** an initial decline in COS emissions across sectors during the early stage of the COVID-19 pandemic,a rapid rebound in emissions occurred following the resumption of economic *** 2021,industrial sources,coal combustion,agriculture and vehicle exhaust accounted for 76.8%,12.3%,10.5%and 0.4%of total COS emissions,*** aluminum industry was the primary COS emitter among industrial sources,contributing40.7% of total ***,Shanxi,and Zhejiang were the top three provinces in terms of anthropogenic COS emissions,reaching 39,21 and 17 Gg S yr-1,***-level regions(hereafter province)with high COS emissions are observed mainly in the eastern and coastal regions of China,which,together with the wind direction,helps explain the pattern of high COS concentrations in the Western Pacific Ocean in *** Green Contribution Coefficient of COS(GCCCOS)was used to assess the relationship between GDP and COS emissions,highlighting the disparity between GDP and COS contributions to green *** part of this analysis,relevant recommendations are proposed to address this *** COS emission inventory in our study can be used as input for the Sulfur Transport and Deposition Model(STEM),reducing uncertainties in the atmospheric COS source?sink budget and promoting understanding of the atmosphere sulfur cycle.
With the success of generative adversarial networks (GANs) on various real-world applications, the controllability and security of GANs have raised more and more concerns from the community. Specifically, understandin...
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Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inherent interference among hybrid distort...
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Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achieved great progress. However, It is sti...
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Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise t...
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