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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Huang, Lijia Qiu, Xiaolan Hu, Donghui Ding, Chibiao Key Laboratory of Spatial Information Processing and Application System Technology China Institute of Electronics Chinese Academy of Sciences 19 Beisihuanxi Road Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)9781424427314

In this paper, an advanced range equation is presented for high-resolution and medium-earth-orbit (MEO) synthetic aperture radar (SAR). Besides an additional linear component, this new equation has a similar formalism to conventional hyperbolic range equation (CHRE), so is called the advanced hyperbolic range equation (AHRE). Incorporating with this additional linear coefficient and the other two parameters, the cubic Taylor series expansion of AHRE has exactly the same value as which of the actual range history. Furthermore, a two-dimensional (2-D) spectrum based on AHRE is derived, which is useful for developing efficient SAR algorithms. Simulations validate AHRE and show its advantages in high-resolution and MEO spaceborne SAR cases. ©2009 IEEE.

关键词： Synthetic aperture radar

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A Novel InSAR phase denoising method via nonlocal wavelet shrinkage

A Novel InSAR phase denoising method via nonlocal wavelet sh...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Dongsheng Fang Xiaolei Lv Bin Lei Key Laboratory of Technology in Geo-spatial Info rmation Processing and Application System Chinese Academy of Sciences Beijing China

In this paper, an interferometric synthetic aperture radar phase denoising method which utilizes both local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks, has been proposed. The derived nonlocal wavelet shrinkage use double L1 norm restrictions, which enforce local and nonlocal sparsity constraints by efficient shrinkage operators. This method can take advantage of the coefficients of nonlocal similarity between group blocks for wavelet shrinkage, and improve the accuracy of filtering result. Experimental results in InSAR phase image denoising tasks with simulation and actual noise data show that the proposed method outperforms the state of the art with lower root-mean-square error and less noisy fringes, making it possible to effectively filtering phase noise with superior performance.

关键词： Filtering Wavelet transforms Noise measurement Noise reduction Coherence Dictionaries

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Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

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Chinese geographical Science 2017年第5期27卷 827-835页

作者： LI Xianju CHEN Gang LIU Jingyi CHEN Weitao CHENG Xinwen LIAO Yiwei College of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China College of Marine Science and Technology China University of GeosciencesWuhan 430074China Laboratory of Geographic Information and Spatial Analysis Department of Geography and PlanningQueen's UniversityKingston ON K7L3N6Canada Faculty of Information Engineering China University of GeosciencesWuhan 430074China School of Electrical and Electronic Engineering Huazhong University of Science and TechnologyWuhan 430074China

Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

关键词： arid region land cover classification RapidEye red-edge band vegetation indices random forest Dunhuang Basin

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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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Towards a better match in siamese network based visual object tracker

arXiv

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arXiv 2018年

作者： He, Anfeng Luo, Chong Tian, Xinmei Zeng, Wenjun CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China Microsoft Research Beijing China

Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance. Despite the great success, this tracking framework still suffers from several limitations. First, it cannot properly handle large object rotation. Second, tracking gets easily distracted when the background contains salient objects. In this paper, we propose two simple yet effective mechanisms, namely angle estimation and spatial masking, to address these issues. The objective is to extract more representative features so that a better match can be obtained between the same object from different frames. The resulting tracker, named Siam-BM, not only significantly improves the tracking performance, but more importantly maintains the realtime capability. Evaluations on the VOT2017 dataset show that Siam-BM achieves an EAO of 0.335, which makes it the best-performing realtime tracker to date. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Aircraft Detection in Sar Images Using Saliency Based Location Regression Network

Aircraft Detection in Sar Images Using Saliency Based Locati...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Wenhui Diao Fangzheng Dou Kun Fu Xian Sun Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

In this paper, a novel framework for aircraft detection in high resolution apron area in Synthetic Aperture Radar (SAR) images is proposed, which combines the strength of location regression based convolutional neural network (CNN) framework and the salient features of target in SAR images. Specifically, a Constant False Alarm Rate (CFAR) based target pre-locating algorithm is introduced, which can match the scale of target in SAR images more accurate compared to the existing region proposal method. In addition, in order to eliminate the fact of overfitting, we explore several strategies for SAR data augmentation, including translation, adding noise and rotation within a small range. Experiments are conducted on the data set acquired by the TerraSAR-X satellite in a resolution of 3.0 meters. The results show that the proposed detection framework could effectively obtain a more accurate detection result.

关键词： Synthetic aperture radar Aircraft Object detection Training Image resolution Radar imaging Atmospheric modeling

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COOPERATIVE HYBRID DIGITAL-ANALOG VIDEO TRANSMISSION IN D2D NETWORKS

COOPERATIVE HYBRID DIGITAL-ANALOG VIDEO TRANSMISSION IN D2D ...

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IEEE International Conference on Image processing

作者： Jian Shen Fei Liang Chong Luo Houqiang Li Wenjun Zeng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China

In this paper, we propose a cooperative video transmission scheme in D2D networks. This research is motivated by the growing interests in hybrid digital-analog video transmissions and device-to-device (D2D) communications. The framework of D2D communications can be generally modeled as a three-node network. In this network, coset coding is used to allow the destination to exploit the correlations between the video signals received in two phases. We have done some work of further optimization to improve the video quality at destination in this network. First, we derive a closed form of the reconstruction error at the destination. This provides a theoretical foundation for finding the optimal quantization step size in coset coding. Then, based on the accurate analysis on the coset coding we design a new power allocation algorithm. Experimental results verify that our scheme outperforms the recently proposed WCVC and DCVC.

关键词： Encoding Resource management Relays Distortion Quantization (signal) Device-to-device communication Decoding

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A TOPSAR Calibration Method for processing System of GF3 Next Generation 6

A TOPSAR Calibration Method for Processing System of GF3 Nex...

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

作者： Yin, Di Han, Bing Sun, Jili Chen, Aiping Zhao, Liangbo Yuan, Xinzhe Zhong, Lihua Hu, Yuxin University of Chinese Academy of Sciences Beijing100049 China Institute of Electronics Chinese Academy of Sciences Beijing100190 China Beijing Institute of Telemetry and Telecommunications Technology Beijing100094 China Chinese Academy of Space Technology Beijing100094 China National Satellite Ocean Application Service State Oceanic Administration Beijing100081 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

TOPSAR is an earth-imaging technique, which can provide wide swath coverage. The paper introduces a TOPSAR focusing and calibrating experiment based on the TOPSAR data acquired by Gaofen3(GF3). In this paper, we firstly derive the processor calibration factors under the demands of keeping signal energy invariant. After that, we fully analyze the impact of antenna electronic steering on TOPSAR products. Aimed to be applied to TOPSAR mode processing system of a SAR satellite, the next generation of GF3, calibration methods to processor and electronic steering was proposed in this paper. © 2019 IEEE.

关键词： Calibration

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A twofold siamese network for real-time object tracking

arXiv

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arXiv 2018年

Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similarity-learning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC [3] allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slide Image Segmentation

DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slid...

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International Conference on Pattern Recognition

作者： Ziqiang Li Rentuo Tao Qianrun Wu Bin Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems University of Science and Technology of China Hefei Anhui China School of foreign studies Hefei University of Technology Hefei Anhui China

Automatic medical image segmentation has wide applications for disease diagnosing. However, it is much more challenging than natural optical image segmentation due to the high-resolution of medical images and the corresponding huge computation cost. The sliding window is a commonly used technique for whole slide image (WSI) segmentation, however, for these methods based on the sliding window, the main drawback is lacking global contextual information for supervision. In this paper, we propose a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments are conducted to evaluate the effectiveness of the proposed method, the results prove that the proposed method can achieve better performance on WSI segmentation compared to methods relying on single-input.

关键词： Knowledge engineering Image segmentation Semantics Breast Feature extraction Optical imaging Pattern recognition

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