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IEEE International Geoscience and Remote Sensing Symposium

作者： Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Synthetic aperture radar (SAR) and optical imaging are different remote sensing methods. Given a SAR image, is it possible to predict what the observed scene looks like in an optical image? Transfer between SAR data and optical data seems to be impossible. However, this article shows examples that by applying deep learning techniques on high resolution airborne SAR images and GoogleEarth optical images, the SAR images and optical images can transfer with each other. The transferring help us to better understand the relationship between SAR and optical image, and can be potentially used to transfer detection or classification algorithms for optical image straightforwardly to be applied on SAR image.

关键词： Image transfer between optical and SAR images Deep learning CycleGAN Generative adversarial networks Transfer learning

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AUTOMATIC WATER DETECTION METHOD IN FLOODING AREA FOR GF-3 SINGLE-POLARIZATION DATA

AUTOMATIC WATER DETECTION METHOD IN FLOODING AREA FOR GF-3 S...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Deke Tang Feng Wang Yuming Xiang Hongjian You Wenchao Kang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

China is a flood disaster-prone country, floods occur almost every year, especially in July and August. Rapid detection and assessment for floods affected areas are of great significance. The Chinese GF-3 SAR satellite, which uses active ground observation technology, has obvious advantages in flood disaster monitoring owing to its all-day, all-weather imaging characteristics. For the purpose of rapid water detection in flooding area, an automatic detection method of flood area based on GF-3 single-polarization SAR data is proposed. The proposed method consists of image preprocessing and water extraction. The experimental results show that the proposed method can realize rapid and accurate extraction of waters in flood disaster area.

关键词： Floods Synthetic aperture radar Data mining Imaging Satellites Manuals Urban areas

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Inshore Ship Detection in Sar Images Based on Deep Neural Networks

Inshore Ship Detection in Sar Images Based on Deep Neural Ne...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lei Liu Guowei Chen Zongxu Pan Bin Lei Quanzhi An Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.

关键词： Marine vehicles Image segmentation Synthetic aperture radar Reliability Merging Image resolution

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SAR TARGET CLASSIFICATION WITH CYCLEGAN TRANSFERRED SIMULATED SAMPLES

SAR TARGET CLASSIFICATION WITH CYCLEGAN TRANSFERRED SIMULATE...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Lei Liu Zongxu Pan Xiaolan Qiu Lingxiao Peng Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Target classification is an important part in automatic target recognition (ATR) systems. Deep learning methods get state of the art performance in SAR target classification. Simulation is a useful data augmentation method when the numbers of real samples for training is not sufficient. This article discusses how to release the full potential of simulated samples which is used to improve performance of SAR target classifier. The proposed method is based on cycle adversarial network (CycleGAN), which can transfer simulated samples to be more similar with real samples in image domain. Experiments show that adding simulated samples straightforward into training dataset is not helpful to improve the performance. However, adding the transferred simulated samples for training results in about 10% increase in accuracy in the designed SAR airplane classification experiment, compared with training without data augmentation.

关键词： Training Testing Synthetic aperture radar Azimuth Gallium nitride Data models Airplanes

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IEEE International Geoscience and Remote Sensing Symposium

作者： Feng Wang Yuming Xiang Xinghui Yao Jiayin Liu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

The conventional shape similarity measurements of remote sensing data face problems in the situation of noise interference, partial information occlusion and missing. A method of shape similarity measurement based on principal curvature enhancement distance transformation is proposed. The distance transformation is carried out to extend the range of the shape contour, improving the robustness of the similarity measure. Besides, to ensure the accuracy of measurement results, the distance map is enhanced by the principal curvature of the shape contour, improving the response of contours with rich information. application experiments of road vectors with GPS data and optical remote sensing images show that the method is effective in practical application.

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An end-to-end foreground-aware network for person re-identification

arXiv

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arXiv 2019年

作者： Liu, Yiheng Zhou, Wengang Liu, Jianzhuang Qi, Guojun Tian, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Noah’s Ark Lab Huawei Technologies Company Limited Shenzhen518129 China Huawei Cloud EI Product Department Cloud & AI Huawei Technologies

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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Curved-path SAR geolocation error analysis based on BP algorithm 38

Curved-path SAR geolocation error analysis based on BP algor...

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38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

作者： Liu, Junbin Qiu, Xiaolan Huang, Lijia Ding, Chibiao Liu, Ming University of Chinese Academy of Sciences China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS China Institute of Electronics Chinese Academy of Sciences China National Disaster Reduction Center Ministry of Civil Affairs NDRCC China

ISBN: (纸本)9781538671504

The theoretical modeling and analysis of SAR location error play an important role in SAR system design and error source budget. Existing SAR geolocation error models are mainly implicit, which are not easy to do analysis, especially in the curved-path case. In this paper, a theoretical explicit model of the relationship between image geolocation error and the path measurement error is established for curved-path SAR, based on BP imaging algorithm. Simulations are given which verify the correctness of the model. The explicit model and the analysis results provide an effective reference for understanding and budgeting the system-level geometric location error for curved-path SAR, such as GeoSAR. © 2018 IEEE

关键词： Geology Imaging Synthetic aperture radar Trajectory Mathematical model Analytical models Simulation

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Passive Super-Low Frequency electromagnetic prospecting technique

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Frontiers of Earth Science 2017年第2期11卷 248-267页

作者： Nan WANG Shanshan ZHAO Jian HUI Qiming QIN Institute of Remote Sensing and GIS School of Earth and Space Sciences Peking University Beijing 100871 China Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China

The Super-Low Frequency （SLF） electromag- netic prospecting technique, adopted as a non-imaging remote sensing tool for depth sounding, is systematically proposed for subsurface geological survey. In this paper, we propose and theoretically illustrate natural source magnetic amplitudes as SLF responses for the first step. In order to directly calculate multi-dimensional theoretical SLF responses, modeling algorithms were developed and evaluated using the finite difference method. The theore- tical results of three-dimensional （3-D） models show that the average normalized SLF magnetic amplitude responses were numerically stable and appropriate for practical interpretation. To explore the depth resolution, three-layer models were configured. The modeling results prove that the SLF technique is more sensitive to conductive objective layers than high resistive ones, with the SLF responses of conductive objective layers obviously show- ing uprising amplitudes in the low frequency range. Afterwards, we proposed an improved Frequency-Depth transformation based on Bostick inversion to realize the depth sounding by empirically adjusting two parameters. The SLF technique has already been successfully applied in geothermal exploration and coalbed methane （CBM） reservoir interpretation, which demonstrates that the proposed methodology is effective in revealing low resistive distributions. Furthermore, it siginificantly contributes to reservoir identification with electromagnetic radiation anomaly extraction. Meanwhile, the SLF inter- pretation results are in accordance with dynamic production status of CBM reservoirs, which means it could provide an economical, convenient and promising method for exploring and monitoring subsurface geo-objects.

关键词： Super-Low Frequency （SLF） three-dimen-sional modeling frequency-depth transformation geother-mal exploration coalbed methane （CBM） electromagneticradiation （EMR）

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Multiscale Progressive Image Compression Network Guided by Learnable Just Noticeable Distortion

Multiscale Progressive Image Compression Network Guided by L...

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IEEE Visual Communications and Image processing (VCIP)

作者： Xin Jin Runchun Ye Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781538644591;9781538644584

One key challenge to the learning-based image compression is that adaptive bit allocation is crucial for compression effectiveness but can hardly be trained into a neural network. Hereby, in this work, We presents an end-to-end trainable image compression framework, named Multi-scale Progressive Network (MPN) to achieve spatially variant bit allocation and rate control through the guidance of a novel learnable just noticeable distortion (JND) map. Specifically, MPN's encoder archives multi-scale feature representation through a three-branched structure. Each branch employs an independent feature extraction strategy for the specific receptive field and merge progressively under the guidance of corresponding learnable JND maps that generated by our proposed Bit-Allocation sub-Network (BAN), which make MPN focus on the areas where attract the human visual system (HVS) and preserve more texture of the image during the compression procedure. Finally, a hybrid objective function is introduced to further make MPN more efficient and mimic the discriminative characteristics of the human visual system (HVS). Experiments show that MPN significantly outperforms traditional JPEG, JPEG 2000 and few state-of-art learning-based methods by multi-scale structural similarity (MS-SSIM) index, and has the ability to produce the much better visual result with rich textures, sharp edges, and fewer artifacts.

关键词： Image coding Feature extraction Transform coding Distortion Image reconstruction Bit rate Visualization

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Learning based Facial Image Compression with Semantic Fidelity Metric

arXiv

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arXiv 2018年

作者： Chen, Zhibo He, Tianyu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework’s efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC’s superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Image compression

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