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International Radar Conference (RADAR)

作者： Chen, Jing Ding, Zegang Wei, Yangkai Gao, Qiang Li, Yong Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing Peoples R China

ISBN: (纸本)9781728126609

In this paper, we propose a novel method for road region detection in high-resolution SAR images based on the fusion of multiple features. Compared with traditional SAR road detection methods with feature fusion, we exploit more useful features such as the standard deviation of directional radiance for distinguishing between roads and buildings or flatland. Then, the features are binarized with dynamic thresholds related to the cumulative possibility distribution of features. Finally, we define a membership parameter to fuse the binarized features and select the road candidate regions according to their geometric features, thereby ensuring better detection rate and lower false alarm rate. Experimental results of GF-3 SAR images show the effectiveness of the proposed method in the detection of both urban and suburban road regions.

关键词： road region detection multiple features dynamic thresholds GF-3 SAR images

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INSHORE SHIP CHANGE DETECTION BASED ON SPATIAL-TEMPORAL SALIENCY 39

INSHORE SHIP CHANGE DETECTION BASED ON SPATIAL-TEMPORAL SALI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ma, Long Liu, Wenchao Han, Zhong Wang, Jue Chen, He Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China

ISBN: (纸本)9781538691540

Automatical inshore ship change detection in optical remote sensing images is meaningful for a wide range of applications, but still a challenging task. In this paper, a visual search inspired model is proposed for inshore ship change detection. The proposed model achieves inshore ship change information extraction based on spatial-temporal saliency detection which mimics visual search mechanism. Experimental results performed on a Google Earth optical remote sensing image data set demonstrate the effectiveness of the proposed model in terms of visual and objective evaluations.

关键词： Terms ship change detection spatial-temporal saliency optical remote sensing image visual search mechanism

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Spatial Enhanced-SSD For Multiclass Object Detection in Remote Sensing Images 39

Spatial Enhanced-SSD For Multiclass Object Detection in Remo...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Guanqun Zhuang, Yin Wang, Zhiru Chen, He Shi, Hao Chen, Liang Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 10087 Peoples R China

ISBN: (纸本)9781538691540

Accurate multiclass object detection in remote sensing images is a challenging task, especially for small objects. Since the scales of objects in remote sensing images have a great variance, almost all of the advanced detection methods have shortcomings. Consequently, improving the accuracy of multiclass objects detection has always been the direction of researchers' efforts. In this paper, a spatial enhanced-Single Shot MultiBox Detector (SE-SSD) is proposed. First, to enhance the spatial information, we enlarge the input image channels with embedding oriented-gradients feature maps. Second, the multiple output layers in the backbone network are changed to reduce one pooling operation. Finally, we design a context module to enhance the receptive field for feature layer description in SE-SSD framework. Experimental results on DOTA dataset demonstrate that Spatial Enhanced-SSD method reaches a much higher mean average precision (mAP) than Faster R-CNN, SSD and other classic detection network.

关键词： Remote Sensing Deep Learning Object Detection Oriented Gradients Context Module Receptive Field

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Modality Translation in Remote Sensing time Series

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Liu, Xun Hong, Danfeng Chanussot, Jocelyn Zhao, Baojun Ghamisi, Pedram Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Sch Informat & Elect Beijing 100081 Peoples R China German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Wessling Germany Univ Grenoble Alpes GIPSA Lab Ctr Natl Rech Sci CNRS Grenoble Inst TechnolGrenoble INP F-38000 Grenoble France Univ Grenoble Alpes Grenoble Inst Technol Ctr Natl Rech Sci CNRS Lab Jean Kuntzmann LJKInriaGrenoble INP F-38000 Grenoble France Helmholtz Zentrum Dresden Rossendorf Machine Learning Grp Helmholtz Inst Freiberg Resource Technol D-09599 Freiberg Germany Inst Adv Res Artificial Intelligence IARAI A-1030 Vienna Austria

Modality translation, which aims to translate images from a source modality to a target one, has attracted a growing interest in the field of remote sensing recently. Compared to translation problems in multimedia applications, modality translation in remote sensing often suffers from inherent ambiguities, i.e., a single input image could correspond to multiple possible outputs, and the results may not be valid in the following image interpretation tasks, such as classification and change detection. To address these issues, we make the attempt to utilizing time-series data to resolve the ambiguities. We propose a novel multimodality image translation framework, which exploits temporal information from two aspects: 1) by introducing a guidance image from given temporally neighboring images in the target modality, we employ a feature mask module and transfer semantic information from temporal images to the output without requiring the use of any semantic labels and 2) while incorporating multiple pairs of images in time series, a temporal constraint is formulated during the learning process in order to guarantee the uniqueness of the prediction result. We also build a multimodal and multitemporal dataset that contains synthetic aperture radar (SAR), visible, and short-wave length infrared band (SWIR) image time series of the same scene to encourage and promote research on modality translation in remote sensing. Experiments are conducted on the dataset for two cross-modality translation tasks (SAR to visible and visible to SWIR). Both qualitative and quantitative results demonstrate the effectiveness and superiority of the proposed model.

关键词： Remote sensing time series analysis Task analysis Synthetic aperture radar Semantics Optical sensors Optical imaging Feature mask module image time series inherent ambiguities modality translation remote sensing

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StfNet: A Two-Stream Convolutional Neural Network for Spatiotemporal Image Fusion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第9期57卷 6552-6564页

作者： Liu, Xun Deng, Chenwei Chanussot, Jocelyn Hong, Danfeng Zhao, Baojun Beijing Inst Technol Sch Informat & Elect Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Wessling Germany TUM Signal Proc Earth Observat SiPEO D-80333 Munich Germany

Spatiotemporal image fusion is considered as a promising way to provide Earth observations with both high spatial resolution and frequent coverage, and recently, learning-based solutions have been receiving broad attention. However, these algorithms treating spatiotemporal fusion as a single image super-resolution problem, generally suffers from the significant spatial information loss in coarse images, due to the large upscaling factors in real applications. To address this issue, in this paper, we exploit temporal information in fine image sequences and solve the spatiotemporal fusion problem with a two-stream convolutional neural network called StfNet. The novelty of this paper is twofold. First, considering the temporal dependence among image sequences, we incorporate the fine image acquired at the neighboring date to super-resolve the coarse image at the prediction date. In this way, our network predicts a fine image not only from the structural similarity between coarse and fine image pairs but also by exploiting abundant texture information in the available neighboring fine images. Second, instead of estimating each output fine image independently, we consider the temporal relations among time-series images and formulate a temporal constraint. This temporal constraint aiming to guarantee the uniqueness of the fusion result and encourages temporal consistent predictions in learning and thus leads to more realistic final results. We evaluate the performance of the StfNet using two actual data sets of Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and both visual and quantitative evaluations demonstrate that our algorithm achieves state-of-the-art performance.

关键词： Convolutional neural network spatiotemporal image fusion super-resolution temporal consistency temporal dependence (TD)

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Adaptive processing at Multiple Frequencies Using the Same Antenna Array Consisting of Dissimilar Nonuniformly Spaced Elements Over an Imperfectly Conducting Ground

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2019年第1期67卷 622-625页

作者： Salama, Dojana Sarkar, Tapan K. Abdallah, Mohammad N. Yang, Xiaopeng Salazar-Palma, Magdalena Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Univ Carlos III Madrid Dept Teoria Senal & Comunicac Madrid 28911 Spain

In this communication, an adaptive processing is carried out simultaneously at multiple frequencies using the same antenna array consisting of nonuniformly spaced dissimilar antenna elements. The antenna array may not be deployed in free space but may be located over an imperfect ground plane. First, to correct the various electromagnetic effects affecting the array performance, an electromagnetic transformation technique is described that can compensate for the various coupling effects in the nonplanar array consisting of nonuniformly spaced dissimilar antenna elements operating over an imperfect ground. Then, a direct data domain least-squares method is used to extract the signal of interest in the presence of fluctuating jammer and clutter simultaneously at multiple frequencies, using a single snap shot of the data. In this procedure, there is no need to have any information about noise or clutter, whatsoever.

关键词： Adaptive antennas direct data domain least-squares (D3LS) approach imperfect ground plane multiple frequencies mutual coupling compensation

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An Efficient Spectral Feature Extraction Framework for Hyperspectral Images

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REMOTE SENSING 2020年第23期12卷 3967-3967页

作者： Li, Zhen Zhao, Baojun Wang, Wenzheng Beijing Inst Technol Sch Informat & Elect Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Beijing Inst Technol Sch Automat 5 Zhongguancun Nandajie Beijing 100081 Peoples R China

Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accuracy. In this paper, we design an effective feature extracting framework for the spectra of hyperspectral data. We construct a structured dictionary to encode spectral information and apply learning machine to map coding coefficients. To reduce training and testing time, the sparsity constraint is replaced by a block-diagonal constraint to accelerate the iteration, and an efficient extreme learning machine is employed to fit the spectral characteristics. To optimize the discriminative ability of our model, we first add spectral convolution to extract abundant spectral information. Then, we design shared constraints for subdictionaries so that the common features of subdictionaries can be expressed more effectively, and the discriminative and reconstructive ability of dictionary will be improved. The experimental results on diverse databases show that the proposed feature extraction framework can not only greatly reduce the training and testing time, but also lead to very competitive accuracy performance compared with deep learning models.

关键词： hyperspectral images efficient feature extraction dictionary learning

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SAR SHIP DETECTION BASED ON RESNET AND TRANSFER LEARNING 39

SAR SHIP DETECTION BASED ON RESNET AND TRANSFER LEARNING

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Yong Ding, Zegang Zhang, Chi Wang, Yan Chen, Jing Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Beijing Inst Space Craft Syst Engn Beijing 100081 Peoples R China

ISBN: (纸本)9781538691540

Synthetic Aperture Radar (SAR) ship detection has been a research hotspot and is significant for marine surveillance. Traditional constant false alarm rate (CFAR) detector has the disadvantages of high false alarm and poor adaptability. Deep learning provides a unique solution for SAR ship detection. However, the traditional deep network cannot reach very deep thus the accuracy is limited, and the training speed is slow. In this paper, a very deep network ResNet with higher accuracy and faster training speed is applied to train the SAR ship detection model. Moreover, transfer learning is applied to combat the small dataset. The proposed method is tested on a general SAR ship dataset and achieves 94.7% average precision. Comparative experiments show that our method has the best performance and which verifies the effectiveness of our method.

关键词： SAR ship detection deep learning ResNet transfer learning

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Interference Removal for Radar/Communication Co-Existence: The Random Scattering Case

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2019年第10期18卷 4831-4845页

作者： Li, Yinchuan Zheng, Le Lops, Marco Wang, Xiaodong Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA Aptiv Elect & Safety Agoura Hills CA 91301 USA Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy

In this paper, we consider an un-cooperative spectrum sharing scenario, where a radar system is to be overlaid to a pre-existing wireless communication system. Given the order of magnitude of the transmitted powers in play, we focus on the issue of interference mitigation at the communication receiver. We explicitly account for the reverberation produced by the (typically high-power) radar transmitter whose signal hits scattering centers (whether targets or clutter) producing interference onto the communication receiver, which is assumed to operate in an un-synchronized and un-coordinated scenario. We first show that the receiver design amounts to solve a joint (non-convex) interference removal and data demodulation problem. Next, we introduce two algorithms exploiting sparsity of a proper representation of the interference and the vector containing demodulation errors of the data block. The first algorithm is basically a relaxed constrained atomic norm minimization, while the latter relies on a two-stage processing structure and is based on alternating minimization. The merits of these algorithms are demonstrated through extensive simulations;interestingly, the two-stage alternating minimization algorithm turns out to achieve satisfactory performance with moderate computational complexity.

关键词： Radar/communication co-existence multi-path atomic norm compressed sensing non-convex blind deconvolution off-grid sparsity

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Parameter estimation and suppression for DRFM-based interrupted sampling repeater jammer

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IET RADAR SONAR AND NAVIGATION 2018年第1期12卷 56-63页

作者： Zhou, Chao Liu, Quanhua Chen, Xinliang Beijing Inst Technol Sch Informat & Elect Beijing Peoples R China Key Lab Embedded Real Time Informat Proc Technol Beijing Peoples R China

By partial interception and multiple forwarding of a radar transmitting signal, digital radio frequency memory-based interrupted sampling repeater jamming can yield a partial processing gain and form multiple false target groups in the range direction, achieving jamming effects of both suppression and deception. Various improved jamming strategies have been proposed, while jamming suppression problems have not been fully addressed. In this study, a jamming suppression method based on the idea of 'reconstruction and cancellation' is proposed by analysing the jamming principle. The method firstly analyses the pulse compression results with time-frequency analysis to obtain the intercepted slice number and forwarding times;then, the slice width is estimated by deconvolution processing;finally, iterative cancellation is used to suppress the jamming. Performance of the method was verified by Monte Carlo simulation. The results show that the normalised error of the slice width estimation is <5% when the jamming-to-noise ratio reaches 15 dB (after pulse compress);the cancellation method can effectively restrain the jamming by bringing an improvement of signal-to-jamming ratio >16 dB.

关键词： deconvolution interference suppression iterative methods jamming Monte Carlo methods parameter estimation radar signal processing signal sampling time-frequency analysis

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