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arXiv 2019年

作者： Shi, Likun Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Deterioration

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Estimation of Building-Integrated Photovoltaic Potential: A Deep Learning Based Approach for Constructing Three-Dimensional Building Models from Satellite Imagery

SSRN

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SSRN 2023年

作者： Yan, Longxu Zhu, Rui Kwan, Mei-Po Luo, Wei Wang, De Zhang, Shangwu Wong, Man Sing You, Linlin Yang, Bishen Chen, Biyu Feng, Ling College of Architecture and Urban Planning Tongji University Shanghai200092 China Key Laboratory of Spatial Intelligent Planning Technology Ministry of Natural Resources Shanghai200092 China Singapore138632 Singapore Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong Geography Department National University of Singapore Singapore117570 Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore117549 Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen518107 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China

Building-integrated photovoltaics (BIPV) are increasingly used to build low-carbon buildings and promote energy transition. However, the absence of precise three-dimensional (3D) building models may hinder accurate estimation of photovoltaic (PV) potential on 3D urban surfaces. This study develops a detail-oriented deep learning approach , which for the first time constructs three-dimensional buildings from high-resolution satellite images and estimates PV potential. Specifically, two deep-learning neural networks, i.e., the Rooftop Segmentation Model and Height Prediction Model, were developed by advancing the basic DeepLabv3+ architecture and integrating dedicated layers, adaptive activation functions, and hybrid losses. Next, the two models were trained and tested on a self-made dataset targeted at Shanghai and an open datasets under standard data augmentation and transfer learning strategies. Then, morphological post-processing procedures were developed to cluster and regularise individual rooftops with estimated heights. Finally, PV potentials in typical areas were estimated and compared. The accuracy assessments suggest satisfactory rooftop segmentation and building height estimation. The PV potentials derived from the actual and predicted building models showed little difference, implying the reliability of the extracted buildings. The proposed model is novel and effective for constructing 3D building models that can facilitate PV penetration and urban studies in various fields. © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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An Approach for Spaceborne InSAR DEM Inversion Integrated with Stereo-SAR Method

An Approach for Spaceborne InSAR DEM Inversion Integrated wi...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Fangfang Li Yueting Zhang Xiaolan Qiu Key Laboratory of Geo-spatial Information Processing and Application System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

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 ground control points (GCP) to obtain absolute interferometric phase as the unwrapped phase is just a relative value. On the other hand, the baseline value used for DEM inversion also needs to be calibrated using GCPs due to the measure error. In practice, it is difficult to manually deploy GCPs especially in the area of steep topography. At present, with the improvement of orbital measure accuracy and system time accuracy for spaceborne SAR satellite, high precise absolute 3D location information of some stable point scatterers can be obtained by stereo SAR using data from different viewing angles. Therefore, this paper combines stereo SAR with InSAR. These point scatters are used as GCPs to calibrate baseline and estimate absolute phase. As a result, high accurate DEM generation without GCPs can be achieved. The experiment using TerraSAR-X data verifies the effectiveness of this method.

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Secrecy relaying strategy over correlated fading channels using CSI estimation 5

Secrecy relaying strategy over correlated fading channels us...

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5th International Conference on Communication and information processing, ICCIP 2019

作者： Chen, Wang Wang, Mingzhi Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Institute of Electronics Chinese Academy of Sciences No. 19 North 4th Ring Road West Haidian District Beijing100191 China

ISBN: (纸本)9781450372589

Cooperative relaying is considered as an effective method to improve the secrecy performance of wireless communications. However, the existing relay selection strategies do not consider the channel correlations between the main and eavesdropping links which limits the secrecy performance. In this paper, the authors explore how to select relay under correlated fading channels in a multiple decode-and-forward (DF) relay network. To this end, the authors adopt the minimum mean squared error (MMSE) method to estimate the channel state information (CSI) of relay-eavesdropper link based on the channel correlation and then propose the single-relay selection (SRS) strategy. Simulation results show that the proposed SRS strategy outperforms other SRS strategies significantly. To further improve the secrecy performance of the DF system, we propose a novel multiple-relay selection (MRS) method for correlated channels taking advantage of estimating the relay-eavesdropper channel. Results show that our MRS strategy performs better than its SRS version and other MRS strategies. We also find that the our MRS strategy gets more performance gain than other strategies with the number of relays increases indicating the effectiveness of estimating the eavesdropping channel in secure relaying. © 2019 Association for Computing Machinery.

关键词： Channel state information

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Stereoscopic omnidirectional image quality assessment based on predictive coding theory

arXiv

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arXiv 2019年

作者： Chen, Zhibo Xu, Jiahua Lin, Chaoyi Zhou, Wei CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China

Objective quality assessment of stereoscopic omnidirectional images is a challenging problem since it is influenced by multiple aspects such as projection deformation, field of view (FoV) range, binocular vision, visual comfort, etc. Existing studies show that classic 2D or 3D image quality assessment (IQA) metrics are not able to perform well for stereoscopic omnidirectional images. However, very few research works have focused on evaluating the perceptual visual quality of omnidirectional images, especially for stereoscopic omnidirectional images. In this paper, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images. Two modules are involved in SOIQE: predictive coding theory based binocular rivalry module and multi-view fusion module. In the binocular rivalry module, we introduce predictive coding theory to simulate the competition between high-level patterns and calculate the similarity and rivalry dominance to obtain the quality scores of viewport images. Moreover, we develop the multi-view fusion module to aggregate the quality scores of viewport images with the help of both content weight and location weight. The proposed SOIQE is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance. Experimental results on our published stereoscopic omnidirectional image quality assessment database (SOLID) demonstrate that our proposed SOIQE method outperforms state-of-the-art metrics. Furthermore, we also verify the effectiveness of each proposed module on both public stereoscopic image datasets and panoramic image datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Binocular vision

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InSAR DEM Reconstruction Based on Backprojection Algorithm in Two Converse Flights 6

InSAR DEM Reconstruction Based on Backprojection Algorithm i...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Hu, Xiaoning Xiang, Maosheng Wang, Bingnan Fu, Xikai University of Chinese Academy of Sciences National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing100190 China National Key Laboratory of Science and Technology on Microwave Imaging Institute of Electronics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distortions such as shadow and layover in the mountainous terrain, which will reduce the quality of generated DEM. Fusion of two or more different aspects of InSAR data can deal with this problem. We propose an InSAR DEM reconstruction method based on backprojection (BP) algorithm in two converse flights. This method utilizes the feature of BP algorithm that geocoding has been realized in imaging process to simplify the fusion process of multi-aspect InSAR data. In addition, an iterative DEM extraction method is introduced to improve DEM accuracy. Experimental results verify the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Online filter clustering and pruning for efficient convnets

arXiv

引用

arXiv 2019年

作者： Zhou, Zhengguang Zhou, Wengang Hong, Richang Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System EEIS Department University of Science and Technology of China HeFei University of Technology

Pruning filters is an effective method for accelerating deep neural networks (DNNs), but most existing approaches prune filters on a pre-trained network directly which limits in acceleration. Although each filter has its own effect in DNNs, but if two filters are same with each other, we could prune one safely. In this paper, we add an extra cluster loss term in the loss function which can force filters in each cluster to be similar online. After training, we keep one filter in each cluster and prune others and fine-tune the pruned network to compensate the loss. Particularly, the clusters in every layer can be defined firstly which is effective for pruning DNNs within residual blocks. Extensive experiments on CIFAR10 and CIFAR100 benchmarks demonstrate the competitive performance of our proposed filter pruning method. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Unambiguous Imaging for Moving Targets in Maritime Scenarios with Dual Receive Channel Mode of GF-3 Satellite

Unambiguous Imaging for Moving Targets in Maritime Scenarios...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Junying Yang Xiaolan Qiu Lihua Zhong Chibiao Ding Lijia Huang Hao Chen Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing China Fifth Laboratory Institute of Communication and Tracking Technology Beijing China

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

Gaofen-3 (GF-3) is the first Chinese multichannel synthetic aperture radar (SAR) sensor that can operate in the dual receive channel (DRC) mode. Different from the traditional single-channel SAR system, the multichannel SAR system can overcome the inherent limitation to achieve high-resolution and wide-swath (HRWS) at the same time. However, the key challenge it faces is false target suppression. Especially for the moving vessels on the ocean, the existence of false targets will increase false alarm probability and affect the interpretation of SAR images. In this paper, the method of integration of detection, velocity estimation, location, and imaging for moving targets in the HRWS SAR system is proposed as well as applied to get an unambiguous image. The simulation and GF-3 real data experimental results show the validity of the method.

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Adaptive disentangled target representation for unsupervised domain adaptation in remote sensing segmentation

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Engineering applications of Artificial Intelligence 2025年 156卷

作者： Lu, Runuo Dong, Shoubin Jia, Jianxin Wang, Xusheng Liu, Kai Chen, Jinsong Guo, Shanxin Zheng, Xiaorou Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Center for Geo-Spatial Information Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application Shenzhen518055 China

Due to the significant differences between the source and target domains, semantic segmentation models for remote sensing images trained on the source domain often struggle to generalize effectively to new target domains, leading to performance degradation. Most solutions disentangle features into domain-invariant features shared by both domains and domain-specific features unique to each domain, relying primarily on the domain-invariant features for model construction and inference. However, during this process, critical domain-specific features as essential information are often discarded as noise, leading to a reduction in segmentation accuracy. To address these issues, this study proposes an unsupervised domain adaptation, called ChameleonRS. ChameleonRS separates the domain-invariant features and the domain-specific features by disentanglement learning, and introduces a multi-level feature alignment module to enhance the representation of domain-invariant features. More importantly, it aligns predictions based on domain-invariant features with those based on fused invariant and specific features using contrastive learning, effectively capturing target-specific features. This study explores two cross-domain semantic segmentation tasks: (I) geographic location variation. (II) geographic location and imaging mode variation. Results show that under setting (I), ChameleonRS outperforms the state-of-the-art method by 4.79% in Intersection over Union (IoU) and 6.51% in the F1 score. Under setting (II), it achieves improvements of 4.46% and 4.82% in IoU and F1 score. Results demonstrate the superiority and adaptability of ChameleonRS under varying geographical and imaging conditions, effectively mitigating the generalization issues caused by distribution differences, and thereby promoting the development and application of remote sensing technologies in practical scenarios. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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An accurate SAR imaging method based on generalized minimax concave penalty 12

An accurate SAR imaging method based on generalized minimax ...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Wei, Zhonghao Zhang, Bingchen Han, Bing Xu, Zhilin Hong, Wen Wu, YiRong University of Chinese Academy of Science and Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Institute of Electronics Chinese Academy of Sciences China

ISBN: (纸本)9783800746361

Sparse signal processing has been applied in synthetic aperture radar (SAR) imaging successfully. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. The underestimation will cause the errors in RCS estimations. In this paper, we present a SAR imaging method based on generalized minimax convex (GMC) penalty, which can avoid the pixel intensity underestimation. The performance of the proposed method is verified using simulations and real data. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Radar cross section

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