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检索条件"机构=CAS Key Laboratory of Technology in Geo-Spatial Information Processing"

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Continual Semantic Segmentation via Mask-Based Class Rebalancing

Continual Semantic Segmentation via Mask-Based Class Rebalan...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongjie Guo Siya Chen Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Continual semantic segmentation (CSS) has risen as a popular field, which aims to acquire new skills constantly without forgetting past knowledge catastrophically. In CSS, we identify that there is a severe imbalance between new classes and old classes, leading to the classifier weight toward new classes. In this paper, we deal with the continual semantic segmentation problem from the class imbalance perspective via mask-based class rebalancing, avoiding the model suffering from catastrophic forgetting. More specifically, the mask-based class rebalancing depends on a mask to combine resampling with reweighting ingenuously, which mitigates the classifier bias toward new classes. Besides, we also propose a frequency knowledge distillation, leveraging multiple frequency components information to maintain the feature representation space for old classes. We demonstrate the effectiveness of our approach with an extensive evaluation of the Pascal-VOC 2012 and ADE20K datasets, significantly outperforming the state-of-the-art method.

关键词： Semantic segmentation Semantics

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The application of the alternate descent conditional gradient method in tomographic SAR off-grid imaging

The application of the alternate descent conditional gradien...

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IET International Radar Conference (IRC 2023)

作者： Mingxiao Shao Cikang Su Zhe Zhang Bingchen Zhang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing People's Republic of China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology and Suzhou Aerospace Information Research Institute Suzhou Jiangsu People's Republic of China

ISBN: (数字)9781837240982

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments.

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On-orbit geometric rectification for micro-satellite based on Lightweight feature database

On-orbit geometric rectification for micro-satellite based o...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Linhui Wang Yuming Xiang Feng Wang Yuxin Hu Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences

On-orbit processing is becoming more prevalent due to its ability to efficiently exploit satellite resources. On-orbit geometric rectification improves positioning accuracy for follow-up tasks such as object detection or geometric calibration, while avoiding heavy burden on downlinking bandwidth and time delay. However, existing rectification methods faces some challenges. The hardware resources onboard satellites are restricted, and geographic positioning is often inaccurate. In this article, we propose a novel method designed for on-orbit rectification. The proposed method introduces a two-step registration framework to overcome large initial offsets and also a feature-compressing strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate that the proposed method performs well in terms of storage space, time efficiency as well as registration accuracy.

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Remote Sensing Image Matching and Localization Method Based on the MySQL Multi-Feature Control Point Database

Remote Sensing Image Matching and Localization Method Based ...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Zhao Zilu Wang Feng You Hongjian Li Peifeng Zhang Tingtao Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

With the increase in the number of remote sensing satellites and imaging modes, the amount of data for acquiring remote sensing images has greatly increased. Effectively and stably performing geometric positioning on remote sensing images is the foundation of remote sensing applications. This paper proposes a remote sensing image matching and positioning method based on a multi-feature control point database in MySQL. Firstly, a feature control point database in MySQL is constructed based on multiple feature methods. Subsequently, the target image is matched from coarse to fine using region features and point features in the feature control point database. Experimental results show that, on three target remote sensing images, the coarse-to-fine matching method based on MySQL multi-feature database can achieve good geometric positioning effects, with a positioning accuracy of around 0.5 pixels.

关键词： Location awareness Accuracy Satellites Databases Image matching Imaging Sensors Remote sensing

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A Novel Reconstruction Filter for the Azimuth Multi-Channel SAR

A Novel Reconstruction Filter for the Azimuth Multi-Channel ...

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2021 CIE International Conference on Radar, Radar 2021

作者： Shang, Mingyang Qiu, Xiaolan Yang, Junying Zhong, Lihua Xin, Yu Ding, Chibiao Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9781665498142

The compensation of channel imbalances plays a vital role in signal processing of the azimuth multi-channel (AMC) synthetic aperture radar (SAR). In the operational AMC SAR system, the channel imbalance is usually considered to be a constant in both the time and frequency domain for one image. However, since the antenna patterns of all channels cannot be strictly consistent, the channel imbalances may vary with the Doppler frequency. This paper establishes the signal model of AMC SAR with channel imbalances changing with Doppler frequency and proposes a novel compensation method during the reconstruction for the echo signals received by all channels. This method has been verified by simulations and real data acquired by Gaofen-3 (GF-3). The result shows that this method has excellent potential for improving the image quality for AMC SAR. © 2021 IEEE.

关键词： Azimuth Multichannel SAR channel imbalances Doppler-varying GF-3 reconstruction

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Radial Velocity Ambiguity Resolution Method in Azimuth Multichannel HRWS SAR

Radial Velocity Ambiguity Resolution Method in Azimuth Multi...

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2021 CIE International Conference on Radar, Radar 2021

作者： Yang, Junying Qiu, Xiaolan Shang, Mingyang Zhong, Lihua Lv, Yini Ding, Chibiao Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Beijing China Aerospace Information Research Institute Chinese Academy of Sciences National Key Laboratory of Microwave Imaging Technology Beijing China

ISBN: (纸本)9781665498142

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. © 2021 IEEE.

关键词： azimuth multichannel SAR HRWS moving target radial velocity ambiguity

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SemanticAnchors: Sequential Fusion using Lidar Point Cloud and Anchors with Semantic Annotations for 3D Object Detection

SemanticAnchors: Sequential Fusion using Lidar Point Cloud a...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhentong Gao Qiantong Wang Zongxu Pan Hui Long Yuxin Hu Zheng Li Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665409858

3D object detection is an important task in autonomous driving scenario, which is the basis of perception and understanding of 3D scenes. LiDAR and camera are two commonly used sensors in 3D object detection tasks. However, using a single sensor to collect data will make some objects difficult to detect because both sensors have insurmountable shortcomings. Unexpectedly, LiDAR-only detection methods tend to show better performance than the multi-sensor methods in public benchmarks. This shows that people need to further explore the methods of combining the data of the two sensors. Recently, PointPainting has been presented to combine the data of LiDAR and camera more efficiently by attaching the result of image semantic segmentation to point cloud data as new channels. In this paper, we propose an error anchor punishment mechanism based on image semantic segmentation results. After the semantic augmentation of the point cloud data, we judge whether the semantic result of each point is correct by traversing the groundtruth boxes. Further, we assign different weights to each anchor according to the error points contained in each anchor. Experimental results on the KITTI valid set show that SemanticAnchors achieves better performance in both 3D and birds eyes view benchmarks. In particular, our method adds little extra computation and achieves performance improvement in all categories.

关键词： Point cloud compression Training Three-dimensional displays Laser radar Semantic segmentation Semantics Object detection

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3D Object Detection Based on Feature Fusion of Point Cloud Sequences

3D Object Detection Based on Feature Fusion of Point Cloud S...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhenyu Zhai Qiantong Wang Zongxu Pan Wenlong Hu Yuxin Hu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665409858

Increasing the number of lines of lidar can alleviate the sparsity of point cloud, but the cost of high-line lidar is higher. From the perspective of algorithm, the fusion of continuous point clouds frames can obtain more abundant information, which is expected to be a means to achieve high-performance object detection on low-line lidar. At present, most multi-frame point cloud fusion methods stack different frames after registration. This fusion method, which introduces GPS, ego-motion, and other information for registration, can only align static objects, not larger moving objects. In this paper, we implement a multi-frame fusion method based on Non-Local, which does not need registration and takes into account both moving and stationary objects. We validated the model performance on the NuScenes dataset. Experimental results show that the performance of the proposed fusion method is better than that of fusion method by stacking frames after registration.

关键词： Point cloud compression Industrial electronics Laser radar Three-dimensional displays Costs Fuses Stacking

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A Method for Analyzing Strong Scattering in SAR Images Based on a Differentiable Simulator

A Method for Analyzing Strong Scattering in SAR Images Based...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zinan Liu Shengren Niu Xiaolan Qiu Lingxiao Peng Chibiao Ding Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China The National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic Aperture Radar (SAR) is extensively employed in earth remote sensing, including both civilian and military sectors. Currently, establishing the correspondence between strong scattering regions in SAR images and the 3D geometry of the target has become a research hotspot. Traditional methods based on scattering center modeling overly rely on prior knowledge and manual annotation. However, neural network-based approaches often lack the constraints of SAR imaging principles when performing feature extraction. In this paper, we propose a method for analyzing strong scattering in SAR images based on a differentiable simulator to visualize the correspondence between strong scattering in SAR images and the 3D geometry of the target. Specifically, the differentiable simulator accumulates the scattering intensity in the scattering simulation stage to generate simulated SAR images at multi-pose. By computing the loss with the SAR image in the dataset (ground truth), the differentiable simulator can provide a direct mapping from the strong scattering in SAR images to the target's 3D structures. The effectiveness of the method is validated on a dataset of simulated SAR images based on the MSTAR. From the result we obtained, the correspondence between the 3D structures of the target model and the strong scattering in the image can be intuitively and clearly determined.

关键词： geometry Training Visualization Solid modeling Three-dimensional displays Scattering Radar polarimetry

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Accelerate Neural Image Compression with Channel-Adaptive Arithmetic Coding

Accelerate Neural Image Compression with Channel-Adaptive Ar...

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IEEE International Symposium on Circuits and Systems

作者： Zongyu Guo Jun Fu Runsen Feng Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

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.

关键词： Image coding Runtime Filtering Rate-distortion Acceleration Integrated circuit modeling Time complexity

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