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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Feng, Aolin Gao, Changsheng Li, Li Liu, Dong Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665438643

High Efficiency Video Coding (HEVC) achieves significant improvement in compression efficiency by introducing quadtree-based block partition. However, in the HEVC reference software-HM, the optimal partition is found by a recursive rate-distortion optimization (RDO) process, which is computationally expensive and not friendly to hardware implementation. We propose a fast block partitioning algorithm using convolutional neural network (CNN) based depth map prediction for HEVC intra coding. We use the depth map to represent the block partition of a coding tree unit (CTU). Then, we design a CNN to predict the depth map for a CTU, and we construct a large-scale dataset to train the CNN. Through the depth map prediction, we obtain a block partitioning structure for the entire CTU, and then we could directly compress each coding unit, getting rid of the recursive RDO process for partitioning. Experimental results show that our proposed method reduces 65.55% encoding time of HM at the cost of 2.02% Bjøntegaard Delta rate (BD-rate) increase on the common test sequences. For 4K sequences, our method achieves 76.97% time saving with 2.89% BD-rate increase. © 2021 IEEE

关键词： Forecasting

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iWavePro: An improved framework for iWave++ 56

iWavePro: An improved framework for iWave++

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56th IEEE International Symposium on Circuits and systems, ISCAS 2023

作者： Xue, Dongmei Dong, Cunhui Ye, Fan Chen, Hang Kang, Bowei Li, Li Liu, Dong University of Science and Technology of China Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Hefei230027 China

ISBN: (纸本)9781665451093

End-to-end image coding methods based on wavelet-like transform have made great progress in recent years. The most advanced one is iWave++, which adopts multi-level lifting schemes based on convolutional neural networks. However, iWave++ still has many unresolved problems. First, the independent entropy coding of each component makes it impossible to use the correlation between components better. Secondly, additive wavelet transform limits the nonlinear ability of learnable wavelet transform. Moreover, the offline training strategy makes the iWave++ unable to adjust according to the content. In this paper, we propose an improved framework for iWave++ called iWave-Pro. iWavePro is designed with several techniques to overcome the problems mentioned above. These techniques are the joint multi-component Gaussian mixture entropy coding, the affine wavelet-like transform, and the online training. Experimental results show that our method can save 10.73% bit rate compared with iWave++ at the same quality. © 2023 IEEE.

关键词： Convolutional neural networks

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Improving wetland mapping by using multi-source data sets

Improving wetland mapping by using multi-source data sets

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2011 International Symposium on Image and Data Fusion, ISIDF 2011

作者： Ji, Luyan Jiang, Kang Geng, Xiurui Tang, Hairong Yu, Kai Zhao, Yongchao Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Science Beijing China

ISBN: (纸本)9781457709692

To conserve and manage wetland resources, it is important to map wetlands and monitor their changes. However, wetland mapping is difficult because of spectral confusion with other landcover classes and spectral variability among different types of wetlands. This paper demonstrated a multi-source based approach for improving the accuracy of wetland mapping. According to the formation of wetland, variables derived from multi-source image date, and other ancillary GIS layers were first integrated to create the possible region for wetland (PRW). And then classification was conducted within the PRW to map wetlands. PRW could greatly reduce the amount of other land cover classes that participated in the classification, therefore reducing both the difficulty of wetland mapping and the commission error rate. In this study, we chose the test area in Dongzhai Harbor, Hainan Island, China, where the main wetland type is mangrove. The mapping was based on LANDSAT TM/ETM+ imagery combined with DEM data, and the ancillary GIS data included available water, soil and vegetation layers. The thematic accuracy of the mapping was assessed using highresolution images from Google Earth and local wetlands databases. Classification of the 2001 Landsat ETM+ scene alone resulted in consumer's accuracy of 65% and Kappa coefficients of 0.69, whereas the multi-source based approach with the same training samples resulted in an accuracy of 86% and 0.80. The developed method is portable, relatively easy to implement, and should be applicable in other landcover classes and over larger extents. © 2011 IEEE.

关键词： Wetlands

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Unambiguous parameter estimation of radial velocity approach for airborne SAR-GMTI

Unambiguous parameter estimation of radial velocity approach...

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2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2011

作者： Xu, Ruipeng Zhang, Dandan Huang, Lijia Hu, Donghui Ding, Chibiao Key Laboratory of Geo-spatial Information Processing and Application System Technology Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9788993246179

The ambiguous Doppler centroid causes incorrect estimation result of the radial velocity. For moving targets with fast radial velocity, an unambiguous estimation approach of the radial velocity is introduced for the airborne SAR system in this paper. First, two channels carried different wavelength can be constructed by range multilook processing (RMLP). Second, the instantaneous frequency curve of the moving target is obtained by polynomial fitting for each channel, where the linear term denotes the baseband Doppler centroid. Considering the fact that the difference of the estimated baseband Doppler centroid in two channels is equal to the difference of the absolute Doppler centroid, the unambiguous radial velocity can be estimated using it. Third, the range walk and the Doppler shift which are produced by the fast radial velocity can be eliminated by compensating a phase in the range-frequency and azimuth-time domain. As a result, the moving target will be reimaged and relocated after azimuth compression. The proposed approach will be applied to both single- and multi-channel SAR system. The simulation results validate the effectiveness of the proposed method. © 2011 KIEES.

关键词： Parameter estimation

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An invariant generalized Hough transform based method of inshore ships detection

An invariant generalized Hough transform based method of ins...

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2011 International Symposium on Image and Data Fusion, ISIDF 2011

作者： Xu, Jian Fu, Kun Sun, Xian Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781457709692

Automatic inshore ship detection from remote sensing imagery has many important applications, such as ship change detection and harbor dynamic surveillance. Stable performance of inshore ship detection is vital to the analysis of ship change and then determines the harbor surveillance effect. However, it is hard to detect inshore ships utilizing the traditional area-based method because the grayscale and texture character of inshore ships are similar to that of the shore. In this paper, a new method based on invariant generalized Hough transform is introduced to extract ship shape using the evidence-gathering procedure. In contrast with other shape extraction methods used in inshore ships detection, our method is specially tolerant to noise and occlusion, and also invariant to translation, scale and rotation transformation. Moreover, our method can be used to separate ships moored together that can benefit to ship recognition. Experiment results are demonstrated on the optical remote sensing imagery from Google Earth. © 2011 IEEE.

关键词： Ships

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Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Improved S2ANet based on attention mechanism for small targe...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lu, Dongdong Tie, Wenjie Lei, Songlin Qiu, Xiaolan Suzhou Aerospace Information Research Institute Laboratory of Spatial Information Intelligent Processing System Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing China

ISBN: (纸本)9781665498142

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions;For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target. © 2021 IEEE.

关键词： Radar remote sensing Radar detection Object detection Radar imaging Optical imaging Feature extraction Adaptive optics

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IMPROVED CALIBRATION METHOD FOR AIRBORNE ATI-SAR BASELINE

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Journal of Electronics(China) 2012年第5期29卷 368-374页

作者： Wang Xin Hong Jun Institute of Electronics Chinese Academy of Sciences Key Laboratory of Geo-spatial Information Processing and Application System Technology Graduate University of Chinese Academy of Sciences

Airborne Along-Track Interferometric Synthetic Aperture Radar (ATI-SAR) baseline error is a main error resource affecting the precision of velocity measurement of moving objects and therefore should be calibrated externally. The Jet Propulsion laboratory (JPL) has proposed a calibration scheme for tasks of PacRim98 and PacRim2000 based on several static objects on the ground. In this paper, the influence of phase center uncertainty on baseline determination by using PacRim method proposed by JPL is analyzed. According to the analysis, the phase center uncertainty can cause a constant part of error to the result of baseline calibration. In order to deal with this problem, an improved calibration method on the basis of sensitivity equations and some ground moving targets, whose velocities are already known, is proposed in this paper. The simulation results show that our proposed calibration method has improved the accuracy of baseline calibration and has obviously prohibited the effect of antennas' phase center uncertainty.

关键词： Airborne Along-Track Interferometric Synthetic Aperture Radar (ATI-SAR) Baseline error Baseline calibration Sensitivity equation

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MULTIPLE KERNEL RELEVANCE VECTOR MACHINE FOR GEOspatial OBJECTS DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES

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Journal of Electronics(China) 2012年第5期29卷 353-360页

作者： Li Xiangjuan Sun Xian Wang Hongqi Li Yu Sun Hao Institute of Electronics Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application System Graduate University Chinese Academy of Sciences

Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.

关键词： Object detection Feature extraction Relevance Vector Machine (RVM) Support Vector Machine (SVM) Sliding-window

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An Approach for Spaceborne InSAR DEM Inversion Integrated with Stereo-SAR Method 6

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

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

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

ISBN: (纸本)9781728129129

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. © 2019 IEEE.

关键词： Synthetic aperture radar

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Sampled-data Observer Design for a Class of Stochastic Nonlinear systems Based on the Approximate Discrete-time Models

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 507-511页

作者： Xinxin Fu Yu Kang Pengfei Li Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science Department of AutomationInstitute of Advanced TechnologyUniversity of Science and Technology of China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Chinese Academy of Sciences IEEE

In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear ***-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear ***,the form of Euler-Maruyama model is simple and easy to be *** results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical *** the effectiveness of the approach is demonstrated by a simulation example.

关键词： Approximation model exponentially bounded sampled-data observer stochastic nonlinear

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