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IEEE International Conference on Radar

作者： Jiang Di Li Xinwu Gong Chen Hong Wen Wu Yirong Aerospace Information Research Institute Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

Supraglacial lake plays an important role in ice sheet dynamics, mass balance and sea level rise. Therefore, it is of great importance to extract supraglacial lake and obtain its spatial-temporal distribution or change. This study provides an automatic extraction model for supraglacial lake, using Synthetic Aperture Radar (SAR) imagery based on deep learning. First, select 19580 Sentinel-1 SAR imagery patches in eight typical areas for manual labeling. Second, the GPU-based U-Net model is used to implement the training of the supraglacial lake, and the results are evaluated in different sites. Finally, the training model is used to perform the supraglacial lake extraction. In addition, this article also introduces ArcticDEM to remove shadow confusion in the margin of the ice sheet. The global-local threshold segmentation method is used to extract the supraglacial lake on the Sentinel-2 MSI imagery, which is a comparative analysis and information supplement for the extracted results in this paper. The results show that: (1) The U-Net network selected in this paper is suitable for processing small sample size SAR data and multi-modal feature extraction. The GPU parallel processing method can achieve rapid extraction of massive data and reduce time cost. (2) The Dice coefficient of the training model reaches 0.98, which can be used for effective extraction of supraglacial lake. (3) Compared with the results of optical image extraction, the algorithm proposed in this paper can identify lakes in areas covered by snow or thin ice, which truly reflects the supraglacial lake temporal and spatial distribution characteristics.

关键词： Training Deep learning Snow Lakes Feature extraction Prediction algorithms Radar polarimetry

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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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Image super-resolution quality assessment: Structural fidelity versus statistical naturalness

arXiv

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arXiv 2021年

作者： Zhou, Wei Wang, Zhou Chen, Zhibo Dept. of Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assessment (IQA) methods that can not only evaluate and compare SISR algorithms, but also guide their future development. In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness. This allows us to observe the behaviors of different SISR algorithms as a tradeoff in the 2D space. Specifically, SISR methods are traditionally designed to achieve high structural fidelity but often sacrifice statistical naturalness, while recent generative adversarial network (GAN) based algorithms tend to create more natural-looking results but lose significantly on structural fidelity. Furthermore, such a 2D evaluation can be easily fused to a scalar quality prediction. Interestingly, we find that a simple linear combination of a straightforward local structural fidelity and a global statistical naturalness measures produce surprisingly accurate predictions of SISR image quality when tested using public subject-rated SISR image datasets. Code of the proposed SFSN model is publicly available at https://***/weizhou-geek/SFSN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Pylon Detection Method Based on Faster R-CNN in High-Resolution SAR Images

A Pylon Detection Method Based on Faster R-CNN in High-Resol...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Lv Xiaoling Jiankun Chen Xiaolan Qiu Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Aerospace Information Research Institute University of Chinese Academy of Sciences Beijing China 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)9781728173344

SyntheticAperture Radar (SAR) is an active microwave imaging system, which can provide all-time and allweather imaging with a wide observation range. In sub-meter high-resolution SAR images, man-made metallic targets, such as pylons, can be easily detected and their details can be obtained. Therefore, pylon detection using high-resolution SAR images has its unique advantages. To solve the problem that traditional pylon detection methods in SAR images require the manual design of feature extractors and perform poorly in terms of realtime requirements, an effective and fast framework for pylon detection in SAR images is proposed in this paper. It combines Faster R-CNN and sliding window algorithm to detect pylons in large-scene SAR images. Besides, it effectively improves detection accuracy by using data augmentation. The proposed method can detect up to 97.6% on the test set and 80% in largescene GF-3 SAR images. The average time cost of detection is 0. 06s for a slice image and 90s for a large-scale SAR image. Experiments demonstrate good performance in detection rate and instantaneity.

关键词： Costs Poles and towers Manuals Radar imaging Feature extraction Microwave theory and techniques Radar polarimetry

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Electromagnetic Simulation Aided SAR Target Classification Via Deep Domain Adaptation

Electromagnetic Simulation Aided SAR Target Classification V...

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IEEE International Conference on Radar

作者： Xiaoling Lv Xiaolan Qiu Wenming Yu Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Suzhou Aerospace Information Research Institute Suzhou China State key Laboratory of Millimeter Waves Southeast University Nanjing China

ISBN: (纸本)9781665468893

Convolutional neural networks (CNNs) have made tremendous success in optical images classification recently. However, in synthetic aperture radar (SAR) target classification, it is difficult to annotate a large amount of real SAR images to train CNNs. Sufficient annotated images can be easily obtained through simulation, but the disparity between the simulated images and the real images makes them difficult to directly apply to the real images classification. In this paper, we propose a model that integrates multi-kernel maximum mean discrepancy (MK-MMD) and domain-adversarial training to alleviate this problem. Simulated SAR images with annotation and unlabeled real SAR images are used to train our model. First, we use domain-adversarial training to prompt the model to extract domain-invariant features. Then, the MK-MMD between the hidden representations of simulated images and real images is reduced to narrow domain discrepancy. Experimental results on the real SAR dataset demonstrate that our method effectively solves the domain shift problem and improves the classification accuracy.

关键词： Training Adaptation models Optical fiber networks Feature extraction Optical imaging Radar polarimetry Data models

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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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A Hybrid and Explainable Deep Learning Framework for SAR Images

A Hybrid and Explainable Deep Learning Framework for SAR Ima...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Zhongling Huang Mihai Datcu Zongxu Pan Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR)

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Deep learning based patch-wise Synthetic Aperture Radar (SAR) image classification usually requires a large number of labeled data for training. Aiming at understanding SAR images with very limited annotation and taking full advantage of complex-valued SAR data, this paper proposes a general and practical framework for quad-, dual-, and single-polarized SAR data. In this framework, two important elements are taken into consideration: image representation and physical scattering properties. Firstly, a convolutional neural network is applied for SAR image representation. Based on time-frequency analysis and polarimetric decomposition, the scattering labels are extracted from complex SAR data with unsupervised deep learning. Then, a bag of scattering topics for a patch is obtained via topic modeling. By assuming that the generated scattering topics can be regarded as the abstract attributes of SAR images, we propose a soft constraint between scattering topics and image representations to refine the network. Finally, a classifier for land cover and land use semantic labels can be learned with only a few annotated samples. The framework is hybrid for the combination of deep neural network and explainable approaches. Experiments are conducted on Gaofen-3 complex SAR data and the results demonstrate the effectiveness of our proposed framework.

关键词： Deep learning Training Annotations Scattering Image representation Radar polarimetry Synthetic aperture radar

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Two-Step Bistatic Spaceborne Sliding-Spotlight SAR Imaging Agorithm Based on Accurate Range Model

Two-Step Bistatic Spaceborne Sliding-Spotlight SAR Imaging A...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Jianbing Xiang Xiaolei Lv Xikai Fu Ye Yun University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The slant range errors caused by traditional hyperbolic range equation (THRE) with stop-and-go assumption will lead to image defocusing in high resolution spaceborne sliding-spotlight Bistatic SAR system (ST-BiSAR). In this paper, an accurate bistatic slant range model based on uniform acceleration curve motion (UARM) is proposed, which is precisely fitted with the actual range history. Then, a two-step imaging algorithm based on UARM and method of reversion series (MSR) is introduced to eliminate aliasing phenomenon and realize focus. Finally, simulation results verify the correctness and effectives of the proposed range model and imaging algorithms.

关键词： Mathematical model Imaging Satellites Synthetic aperture radar Acceleration Manganese Azimuth

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Multi-aspect Tomographic SAR Imaging Approach via Distributed Compressed Sensing and Joint Sparsity

Multi-aspect Tomographic SAR Imaging Approach via Distribute...

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IEEE International Conference on Radar

作者： Bang Du Zhe Zhang Xiaolan Qiu Bin Lei Chibiao Ding Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute (of Chinese Academy of Sciences) University of Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China Aerospace Information Research Institute (of Chinese Academy of Sciences) Beijing China

ISBN: (纸本)9781665468893

Synthetic aperture radar (SAR) tomography (TomoSAR) is a novel technique that enables three-dimensional (3-D) imaging and plays an important role in urban remote sensing by utilizing multiple observations of the same target scene from various baselines. Canonical TomoSAR observations are from a single aspect, which has been well studies already. However, modern SAR sensors such as Unmanned Aerial Vehicle (UAV) allow us to achieve multi-aspect TomoSAR data of the same target scene. This paper proposes a novel framework named “Multi-aspect TomoSAR,” which takes advantage of rich TomoSAR data from multiple observation aspects. We derive the multi-aspect TomoSAR signal model using distributed compressed sensing (DCS) and adopt a simultaneous sparse approximation algorithm named SOMP to solve the joint sparsity model. Numerical results on synthetic simulated data show that the multi-aspect estimation can provide more accurate estimation, yield a promising perspective. Experimental results on real airborne data will be reported in the journal version of this work later.

关键词： Atmospheric modeling Airborne radar Estimation Tomography Autonomous aerial vehicles Sparse representation Numerical models

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Sasl: Saliency-adaptive sparsity learning for neural network acceleration

arXiv

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arXiv 2020年

作者： Shi, Jun Xu, Jianfeng Tasaka, Kazuyuki Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China KDDI Research Japan

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in an optimal structuresparse network. In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization. A novel and effective estimation of each filter, i.e., saliency, is designed, which is measured from two aspects: The importance for the prediction performance and the consumed computational resources. During sparsity learning, the regularization strength is adjusted according to the saliency, so our optimized format can better preserve the prediction performance while zeroing out more computation-heavy filters. The calculation for saliency introduces minimum overhead to the training process, which means our SASL is very efficient. During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency. Extensive experiments demonstrate the superior performance of our method. Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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