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15th European Conference on Synthetic Aperture Radar, EUSAR 2024

作者： Li, Jie Li, Zhiyuan Fan, Yizhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China

ISBN: (纸本)9783800762873

The current synthetic aperture radar (SAR) images with ultra high resolution provide the detailed structures of the urban areas, which are often utilized to retrieve 3D spatial information of the detailed structures based on tomographic synthetic aperture radar (TomoSAR). Compressive sensing (CS), as a favorable sparse reconstruction technique has been widely used in TomoSAR, however, the sparsity constraints by CS algorithms often lose detailed structures of the targets due to the pixel-wise processing. In this paper, we apply the morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization can shrink the concatenations caused by outliers in the iterative reconstruction, enhancing the detailed structural properties of reconstructed targets. And the alternating direction method of multipliers (ADMM) is applied, where the Bregman iteration is adopted for solving the sub-problem with the morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and preserve the detailed structures of tomographic reconstruction, leading to improved correctness and completeness. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Morphology

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Residual objectness for imbalance reduction

arXiv

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arXiv 2019年

作者： Chen, Joya Liu, Dong Luo, Bin Peng, Xuezheng Xu, Tong Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Tencent TaiQ Team

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to alleviate the imbalance, they are usually heuristic and demand laborious hyper-parameters tuning, which is hard to achieve the optimality. In this paper, we first reveal that such the imbalance could be addressed in a learning-based manner. Guided by this illuminating observation, we propose a novel Residual Objectness (ResObj) mechanism that addresses the imbalance by end-to-end optimization, while no further hand-crafted sampling/reweighting is required. Specifically, by applying multiple cascaded objectness-related modules with residual connections, we formulate an elegant consecutive refinement procedure for distinguishing the foregrounds from backgrounds, thereby progressively addressing the imbalance. Extensive experiments present the effectiveness of our method, as well as its compatibility and adaptivity for both region-based and one-stage detectors, namely, the RetinaNet-ResObj, YOLOv3-ResObj and FasterRCNN-ResObj achieve relative 3.6%, 3.9%, 3.2% Average Precision (AP) improvements compared with their vanilla models on COCO, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Analysis of the Fluence of Multi-sources DEM in Remote Sensing Image Ortho-rectification

Analysis of the Fluence of Multi-sources DEM in Remote Sensi...

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2024 International Conference on Remote Sensing, Mapping, and Image processing, RSMIP 2024

作者： Liu, Rui Zhu, Jingxing Jiao, Niangang Wang, Feng You, Hongjian Xiang, Yuming Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510680012

Digital Elevation Models (DEMs) depict the configuration of the Earth surface, which is essential for remote sensing image ortho-rectification. Nowadays, the ALOS DEM, ASTER GDEM and SRTM DEM are the most commonly used models, produced based on different remote sensing datasets. geolocation errors may be introduced with different producing methods and datasets. Therefore, multi-sources remote sensing images covering various terrain in China are experimented for the analysis of ortho-rectification accuracy using different DEMs. Based on the rational function model, the geolocation error of original imagery can be reduced greatly with the grounds control points, and different DEMs are used for the ortho-rectification of different imagery. The accuracy is experimented and verified using high-precision reference images. Experimental results show that ortho-rectificated images with the ALOS DEM can achieve better accuracy compared with the SRTM DEM and ASTER GDEM, which is significant for geometric processing of remote sensing images. © 2024 SPIE.

关键词： Remote sensing

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Layer-wise coordination between encoder and decoder for neural machine translation 18

Layer-wise coordination between encoder and decoder for neur...

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Proceedings of the 32nd International Conference on Neural information processing systems

作者： Tianyu He Xu Tan Yingce Xia Di He Tao Qin Zhibo Chen Tie-Yan Liu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Microsoft Research Key Laboratory of Machine Perception MOE School of EECS Peking University

Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures. In this paper, we propose the concept of layer-wise coordination for NMT, which explicitly coordinates the learning of hidden representations of the encoder and decoder together layer by layer, gradually from low level to high level. Specifically, we design a layer-wise attention and mixed attention mechanism, and further share the parameters of each layer between the encoder and decoder to regularize and coordinate the learning. Experiments show that combined with the state-of-the-art Transformer model, layer-wise coordination achieves improvements on three IWSLT and two WMT translation tasks. More specifically, our method achieves 34.43 and 29.01 BLEU score on WMT16 English-Romanian and WMT14 English-German tasks, outperforming the Transformer baseline.

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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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A Multiple Moving Ships Detection Method for GF-4 Satellite Image in Thin-Cloud Environment 1

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6th China High Resolution Earth Observation Conference, CHREOC 2019

作者： Lv, Peng Hu, Yuxin Li, Qianqian Hou, Yangshuan Wang, Xiaohui Lei, Bin Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9789811539473

ISBN: (纸本)9789811539466

At present, the GF-4 satellite is the world’s highest resolution geostationary orbit optical imaging satellite. The GF-4 satellite has the advantages of wide-swath and high-frequency imaging, so it can provide quasi-real-time images for ship surveillance. In fact, using the GF-4 satellite image to detect moving ships is to detect the gray value of the wake formed by the ship moving at sea. However, because of the geometric features of the ship’s wake vary with the speed, the ship detection method based on geometric features is not applicable. Since, not only the ship is moving in the image but also the cloud is moving all the time, the optical flow method is not applicable. Ship detection methods based on machine learning and deep learning are also not applicable due to too few texture features of the wake. In this paper, we present a novel method for ship detection in thin-cloud environments. The real image processing of GF-4 satellite shows that the proposed method can detect multiple moving ships accurately and quickly in a thin-cloud environment. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ships

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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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SAR Change Detection Based on keypoint Graph Structure and Cosegmentation-Based Refinement

SAR Change Detection Based on Keypoint Graph Structure and C...

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geology, Mapping and Remote Sensing (ICGMRS), International Conference on

作者： Jingxing Zhu Rui Liu Niangang Jiao Feng Wang Hongjian You Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350365719

ISBN: (纸本)9798350365726

Change detection between high-resolution Synthetic Aperture Radar (SAR) images remains a challenging task. Due to the effectively integration of structure and intensity information, graph-based methods have made some progress in SAR change detection. But it still faces the issues of robustness of graph construction and incomplete change detection outcomes. To tackle these challenges, we propose an improved keypoint-based graph structure change detection approach. First, we propose a keypoint extraction strategy guided by a coarse change map to better describe potential change areas. Second, we construct stable graph structures on these keypoints with employing a robust distance measurement that considers the statistical properties of SAR images. Then, the keypoint difference image (KDI) is derived through measuring the consistency of graph structures between pre-and post-time SAR images. Next, keypoint change map (KCM) is generated through clustering KDI by k-means. Finally, we use co-segmentation superpixel mask to refine the KCM and obtain accurate and complete binary change map. We validate the effective of proposed framework on GF-3 dataset. Experimental results demonstrate the superior performance of our method compared to alternative method.

关键词： Image segmentation geology Noise Focusing Speckle Radar polarimetry Robustness

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Analysis of designed composite scatterer on high resolution PolSAR image

Analysis of designed composite scatterer on high resolution ...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Hu Ding-sheng Qiu Xiao-Ian Li Fang-fang Ding Chi-Biao Institute of Electronics Chinese Academy of Sciences (IECAS) Beijing China University of Chinese Academy of Sciences (UCAS) Beijing China Key Laboratory of Technology on Geo-spatial Information Processing and Application System Beijing 100190 China Key Laboratory of Technology on Geo-spatial Information Processing and Application System Beijing China

Multiple scattering may render synthetic aperture radar (SAR) image interpretation difficult, particularly when it comes to imaging of man-made structures, which can be modeled as composite scatterers. To isolate different scattering mechanism, we designed an airborne SAR experiment based on the high resolution, sub-metric to decimetric range, capabilities full-polarimetric SAR system, CARSS (Chinese Airborne Remote Sensing system), developing by IEcas. The imaging results are quite accord with the theoretical analysis. With polarimetric target decomposition, we can simply distinguish the different scattering mechanism. The idea to interpret the man-made targets as the combination of simple scattering mechanisms is supported by the experiment results.

关键词： Scattering Synthetic aperture radar Imaging Image color analysis Image resolution Radar imaging Remote sensing

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