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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

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

ISBN: (数字)9781728129129

ISBN: (纸本)9781728129136

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.

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OBJECT DETECTION WITH HEAD DIRECTION IN REMOTE SENSING IMAGES BASED ON ROTATIONAL REGION CNN

OBJECT DETECTION WITH HEAD DIRECTION IN REMOTE SENSING IMAGE...

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IEEE International geoscience and Remote Sensing Symposium

作者： Xue Yang Kun Fu Hao Sun Xian Sun Menglong Yan Wenhui Diao Zhi Guo Electrical and Communication Engineering 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

Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.

关键词： Object detection Rotating region Convolution neural network Prediction Non-maximal suppression

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RELAXATION LABELLING BASED LAND MASKING IN SAR IMAGES

RELAXATION LABELLING BASED LAND MASKING IN SAR IMAGES

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IEEE International geoscience and Remote Sensing Symposium

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

In this paper, a relaxation labelling based land masking method is proposed for separating sea and land in SAR images. Land masking, also known as sea-land segmentation, is a part of ship detection system for SAR images to avoid detecting false alarms in the land. Relaxation labeling is an iterative method, which can separate foreground pixels from background ones using the neighborhood information of pixels in the image. When relaxation labelling converges, the segmented result is often unsatisfactory, since it tends to label more foreground pixels. To overcome this issue, a loss composed of the background probability distribution diversity and the gradient magnitude of the result is introduced to indicate when to stop the iteration. Experimental results on several Gaofen-3 SAR images demonstrate the effectiveness of the proposed method.

关键词： Labeling Image segmentation Synthetic aperture radar Marine vehicles Probability distribution Morphology Iterative methods

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ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIMETRIC CIRCULAR SAR MULTI-ASPECT SIGNATURES

ANISOTROPIC SCATTERING DETECTION FOR CHARACTERIZING POLARIME...

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IEEE International geoscience and Remote Sensing Symposium

作者： Yang Li Yun Lin Wen Hong Ran Xu Zhimin Zhuo Qiang Yin Beijing Institute of Electronic System Engineering Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Science Beijing China Beijing University of Chemical Technology Beijing China

Circular synthetic aperture radar (CSAR) can provide distinctive multi-aspect anisotropic scattering signatures. However, it is impossible to retain the anisotropic signatures in a SAR image that combines all the subapertures coherently or incoherently. In this letter, we propose a polarimetric CSAR anisotropic scattering detection framework to characterize multi-aspect and fully polarimetric SAR signatures of point-like and distributed targets. We applied this framework to quantify and rank media polarimetric scattering dissimilarity over all aspects and to determine whether the most different one shows anisotropy by use of constant false alarm rate (CFAR) detection. Furthermore, we demonstrated the monotonicity of CFAR detection function and incorporated this function to decrease the complexity of the anisotropic scattering test. Our algorithm was validated and applied to a set of airborne P-band fully polarimetric circular SAR data acquired by the Institute of Electronics, Chinese Academy of Science (IECAS). The results indicate the framework can retain anisotropic scattering and extract a series of new multi-aspect polarimetric SAR signatures for terrain classification.

关键词： Scattering Apertures Synthetic aperture radar Covariance matrices Radar polarimetry Image color analysis Optical imaging

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HIGH QUALITY REMOTE SENSING IMAGE SUPER-RESOLUTION USING DEEP MEMORY CONNECTED NETWORK

HIGH QUALITY REMOTE SENSING IMAGE SUPER-RESOLUTION USING DEE...

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IEEE International geoscience and Remote Sensing Symposium

作者： Wenjia Xu Guangluan Xu Yang Wang Xian Sun Daoyu Lin Yirong Wu Electrical and Communication Engineering 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

Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification. However, existing methods based on the neural network usually have small receptive fields and ignore the image detail. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images. We build local and global memory connections to combine image detail with environmental information. To further reduce parameters and ease time-consuming, we propose down-sampling units, shrinking the spatial size of feature maps. We test DMCN on three remote sensing datasets with different spatial resolution. Experimental results indicate that our method yields promising improvements in both accuracy and visual performance over the current state-of-the-art.

关键词： spatial resolution Remote sensing Image reconstruction Training Convolutional neural networks Satellites

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RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS for DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA

RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS for DEEP SE...

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2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"

作者： Chen, K. Weinmann, M. Gao, X. Yan, M. Hinz, S. Jutzi, B. Weinmann, M. Key Laboratory of Spatial Information Processing and Application System Technology Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Computer Science II University of Bonn Bonn Germany Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation. © 2018 Copernicus GmbH. All rights reserved.

关键词： Aerial Imagery CNN Deep Learning Multi-Modal Data Residual Network Semantic Segmentation

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ROAD EXTRACTION FROM REMOTE SENSING IMAGES BY MULTIPLE FEATURE PYRAMID NETWORK

ROAD EXTRACTION FROM REMOTE SENSING IMAGES BY MULTIPLE FEATU...

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IEEE International geoscience and Remote Sensing Symposium

作者： Xun Gao Xian Sun Menglong Yan Hao Sun Kun Fu Yue Zhang Zhipeng Ge 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 Chinese Academy of Sciences Beijing China

Road extraction from high-resolution remote sensing images has been applied in many domains, but it is still full of challenges. We focus on the problem of slender roads, proposing a new multiple feature pyramid network (MFPN), which is composed of an effective feature pyramid and the tailored pyramid pooling module based on PSPNet. These two designs can address the sparsity of roads in remote sensing images via using multi-level semantic features. Experiments on remote sensing images from Quick Bird show that our MFPN model achieves competitive performance, especially for slender roads.

关键词： Roads Feature extraction Remote sensing Convolution Semantics Image segmentation Data mining

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A3Net: Adversarial-and-attention network for machine reading comprehension

arXiv

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arXiv 2018年

作者： Wang, Jiuniu Fu, Xingyu Xu, Guangluan Wu, Yirong Chen, Ziyan Wei, Yang Jin, Li Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics CAS Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

In this paper, we introduce Adversarial-and-attention Network (A3Net) for Machine Reading Comprehension. This model extends existing approaches from two perspectives. First, adversarial training is applied to several target variables within the model, rather than only to the inputs or embeddings. We control the norm of adversarial perturbations according to the norm of original target variables, so that we can jointly add perturbations to several target variables during training. As an effective regularization method, adversarial training improves robustness and generalization of our model. Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. Combining these two contributions, we enhance the diversity of dataset and the information extracting ability of the model at the same time. Meanwhile, we construct A3Net for the WebQA dataset. Results show that our model outperforms the state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%). Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

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DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REMOTE SENSING IMAGE INPAINTING

DENSE-ADD NET: AN NOVEL CONVOLUTIONAL NEURAL NETWORK FOR REM...

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IEEE International geoscience and Remote Sensing Symposium

作者： Daoyu Lin Guangluan Xu Yang Wang Xian Sun Kun Fu 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 Chinese Academy of Sciences Beijing China

Through the recent performance of convolutional neural networks in image processing tasks, we propose a deep fully convolutional network for remote sensing image inpainting. The proposed Dense-Add Net (Dense-Add Network) can alleviate the vanishing-gradient problem, strengthen feature reuse, and substantially reduce the memory usage. We apply residual learning to learn the mappings from corrupted image to recovered image directly;it will back-propagate gradient to the bottom layers and accelerate the training process. We train the proposed Dense-Add Net with a robust Charbonnier loss function which can achieve high-quality reconstruction. The experimental verify the efficacy of our proposed Dense-Add Net.

关键词： Remote sensing Training Acceleration Image reconstruction Painting Convolution Interpolation

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A HYBRID NEURAL NETWORK FOR CHROMA INTRA PREDICTION

A HYBRID NEURAL NETWORK FOR CHROMA INTRA PREDICTION

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IEEE International Conference on Image processing

作者： Yue Li Li Li Zhu Li Jianchao Yang Ning Xu Dong Liu Houqiang Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China University of Missouri Kansas City MO USA Snapchat Inc. CA USA

For chroma intra prediction, previous methods exemplified by the Linear Model method (LM) usually assume a linear correlation between the luma and chroma components in a coding block. This assumption is inaccurate for complex image content or large blocks, and restricts the prediction accuracy. In this paper, we propose a chroma intra prediction method by exploiting both spatial and cross-channel correlations using a hybrid neural network. Specifically, we utilize a convolutional neural network to extract features from the reconstructed luma samples of the current block, as well as utilize a fully connected network to extract features from the neighboring reconstructed luma and chroma samples. The extracted twofold features are then fused to predict the chroma samples-Cb and Cr simultaneously. The proposed chroma intra prediction method is integrated into HEVC. Preliminary results show that, compared with HEVC plus LM, the proposed method achieves on average 0.2%, 3.1% and 2.0% BD-rate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration.

关键词： Feature extraction Neural networks Kernel Correlation Image reconstruction Encoding Prediction methods

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