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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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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Rain removal is important for many computer vision applications, such as surveillance, autonomous car, etc. Traditionally, rain removal is regarded as a signal removal problem which usually causes over-smoothing by re...
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Offshore wind is an important source of information for monitoring the interaction between fishery and marine water vapor environment. In this paper, the CMOD5 function was used to invert the wind field in the coastal...
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In this paper,nine indicators selected from three perspectives(convenience,environmental and location characteristics)and three regression models(OLS,SLM and SEM)are used to explore the influencing factors of housing ...
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In this paper,nine indicators selected from three perspectives(convenience,environmental and location characteristics)and three regression models(OLS,SLM and SEM)are used to explore the influencing factors of housing sales vacancy in the Guangzhou Metropolitan Area,*** results show that subway accessibility,peripheral aversion municipal facilities and distance from the CBD(Central Business District)are consistent with theoretical *** accessibility is negatively correlated with the housing sales vacancy rates,while peripheral aversion municipal facilities and distance from the CBD are positively correlated with housing vacancy rates.
SyntheticAperture Radar (SAR) is an active microwave imaging system, which can provide all-time and allweather imaging with a wide observation range. In sub-meter high-resolution SAR images, man-made metallic targets,...
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ISBN:
(纸本)9781728173344
SyntheticAperture Radar (SAR) is an active microwave imaging system, which can provide all-time and allweather imaging with a wide observation range. In sub-meter high-resolution SAR images, man-made metallic targets, such as pylons, can be easily detected and their details can be obtained. Therefore, pylon detection using high-resolution SAR images has its unique advantages. To solve the problem that traditional pylon detection methods in SAR images require the manual design of feature extractors and perform poorly in terms of realtime requirements, an effective and fast framework for pylon detection in SAR images is proposed in this paper. It combines Faster R-CNN and sliding window algorithm to detect pylons in large-scene SAR images. Besides, it effectively improves detection accuracy by using data augmentation. The proposed method can detect up to 97.6% on the test set and 80% in largescene GF-3 SAR images. The average time cost of detection is 0. 06s for a slice image and 90s for a large-scale SAR image. Experiments demonstrate good performance in detection rate and instantaneity.
Spaceborne Interferometric Synthetic Aperture Radar (InSAR) has the capability of high precise topographic mapping for large area. However, on the one hand, digital elevation models (DEM) inversion needs at least one ...
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The inspection procedure of Chinese high voltage power grid is mainly based on human inspection for many years. This is not only time-consuming and difficult, but the inspection results are also not objective and comp...
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Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assessment (IQA) methods that can not only ...
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Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same ...
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ISBN:
(纸本)9781665468893
Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named “Multi-aspect TomoSAR,” which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later.
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