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Discovering bursty topics from news stream with a unified statistical model

Journal of Computational information systems 2014年第18期10卷 7825-7832页

作者： Qi, Xiang Huang, Yu Liu, Xiaoyan Fu, Kun Wang, Hongqi Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics University of Chinese Academy of Sciences Beijing China

This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM). However, Kleinberg's model is originally proposed for the burst of keywords, the frequency counts it models are not proper to describe the burst states of topics, leading to some unwanted results. A more reasonable way is to model the influence burst states put on each document's topic distribution. Considering this, we propose a unified statistical model that takes the burst states as markov latent variables that influence the topic allocation of documents. We derive a Gibbs sampling algorithm for the proposal. Experiment results confirm our model's advantages both qualitatively and quantitatively.

关键词： Bayesian networks

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MEDIUM-EARTH-ORBIT SAR IMAGING BASED ON keySTONE TRANSFORM AND AZIMUTH PERTURBATION

MEDIUM-EARTH-ORBIT SAR IMAGING BASED ON KEYSTONE TRANSFORM A...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Lijia HUANG Bing HAN Donghui HU Chibiao DING Lihua ZHONG Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences

ISBN: (纸本)9781467311601

Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.

关键词： Azimuth perturbation (AP) keystone transform (KT) Medium-Earth-orbit (MEO) SAR SAR data processing Synthetic aperture radar (SAR)

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Ionosphere Estimation and Correction for Geosynchronous SAR based on point target deviation between Range Sub-images

Ionosphere Estimation and Correction for Geosynchronous SAR ...

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2016 IEEE 13th International Conference on Signal processing（ICSP2016）

作者： Huang Lijia Ding Chibiao Qiu Xiaolan Zhang Hongyi Li Liang Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of ElectronicsChinese Academy of Sciences

Ionosphere is an important factor in highresolution spaceborne synthetic aperture radar（SAR） and geosynchronous（GEO） SAR. An approach based on point target deviation between range sub-images is proposed in this paper to estimate and correct the ionosphere. Due to the dispersion effect, the ionosphere causes propagation delay deviation in range subimages with different carrier frequency. This deviation can be used to estimate the total electron content（TEC） along the propagation path, and then the ionospheric effects can be corrected according to the signal model deduced out in this paper. The simulation results show that our approach is valid and robust.

关键词： Spaceborne Synthetic Aperture Radar（SAR） Geosynchronous（GEO） SAR Ionospheric Effects Estimation and Correction

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SEMI-SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

SEMI-SUPERVISED OBJECT DETECTION IN REMOTE SENSING IMAGES US...

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IEEE International Geoscience and Remote Sensing Symposium

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

Object detection is a challenging task in computer vision. Now many detection networks can get a good detection result when applying large training dataset. However, annotating sufficient amount of data for training is often time-consuming. To address this problem, a semi-supervised learning based method is proposed in this paper. Semi-supervised learning trains detection networks with few annotated data and massive amount of unannotated data. In the proposed method, Generative Adversarial Network is applied to extract data distribution from unannotated data. The extracted information is then applied to improve the performance of detection network. Experiment shows that the method in this paper greatly improves the detection performance compared with supervised learning using only few annotated data. The results prove that it is possible to achieve acceptable detection result when only few target object is annotated in the training dataset.

关键词： Gallium nitride Generative adversarial networks Detectors Generators Airplanes Training Semisupervised learning

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AUTOMATIC MODULATION RECOGNITION BASED ON SST AND DEEP LEARNING MODEL

AUTOMATIC MODULATION RECOGNITION BASED ON SST AND DEEP LEARN...

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IET International Radar Conference 2023, IRC 2023

作者： Xiang, Jianbing Lv, Xiaolei Han, Bing Zhong, Lihua Fang, Linlin Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in GeoSpatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

As the electromagnetic environment in battlefields becomes increasingly complex, automatic modulation recognition for noncooperation radiation source signal is becoming vital and challenging. Most previous works were more likely to cause low recognition accuracy with high computational complexity in low signal-to-noise ratio (SNR), and only considered spatial or temporal attributes of signal which ignores the feature interaction. In this paper, we firstly apply synchrosqueezing transform (SST) to extract high resolution and robustness time-frequency feature as one of signal representation, and convert the signal into the in-phase/quadrature (I/Q) components as another signal representation, which enhances the diversity of signal representation. Then, we propose a deep learning model to achieve automatic recognition, which has two streams and each stream consists of the convolutional neural network (CNN), bistatic long short-term memory (BiLSTM) and temporal attention (TAT). It efficiently explores the feature from each signal representation and enhances the key features by the introduction of TAT, furthermore, it implements feature interaction to form a new feature matrix. Finally, experiments and analysis were conducted using the RadioML2016.10a dataset. The experimental results showed that compared to other algorithms, this method has higher robustness and perform better recognition accuracy under low SNR. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

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An Invariant Generalized Hough Transform Based Method of Inshore Ships Detection

An Invariant Generalized Hough Transform Based Method of Ins...

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International Symposium on Image and Data Fusion (ISIDF)

作者： Jian Xu Kun Fu Xian Sun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Automatic inshore ship detection from remote sensing imagery has many important applications, such as ship change detection and harbor dynamic surveillance. Stable performance of inshore ship detection is vital to the analysis of ship change and then determines the harbor surveillance effect. However, it is hard to detect inshore ships utilizing the traditional area-based method because the grayscale and texture character of inshore ships are similar to that of the shore. In this paper, a new method based on invariant generalized Hough transform is introduced to extract ship shape using the evidence-gathering procedure. In contrast with other shape extraction methods used in inshore ships detection, our method is specially tolerant to noise and occlusion, and also invariant to translation, scale and rotation transformation. Moreover, our method can be used to separate ships moored together that can benefit to ship recognition. Experiment results are demonstrated on the optical remote sensing imagery from Google Earth.

关键词： Marine vehicles Image edge detection Transforms Noise Remote sensing Indexes Shape

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DRBOX FAMILY: A GROUP OF OBJECT DETECTION TECHNIQUES FOR REMOTE SENSING IMAGES

DRBOX FAMILY: A GROUP OF OBJECT DETECTION TECHNIQUES FOR REM...

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IEEE International Geoscience and Remote Sensing Symposium

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

Objects in remote sensing images are difficult to detect due to arbitrarily rotated angles and the wide variance of scales. As bounding box plays an important role in object detection, we proposes a new bounding box type named rotated bounding box (rBox). With the application of rBox, we have proposed a series of detection techniques (DrBox, DrBoxLight, DrBoxSemi, DrBoxPro) to effectively handle the situation where the orientation angles of the objects are arbitrary. This article is a brief overview of these techniques. The original DrBox detector applies VGG-net as its main network framework, with image pyramid input to address multi-scale problem. DrBoxLight is a mini version of DrBox, which applies MobileNet and knowledge distillation to be deployed on embedded devices. DrBoxSemi is a semi-supervised version of DrBox, so annotation of all training samples is no longer necessary. DrBoxPro is the most important update for DrBox with professional designing of abundant prior-rBoxes on feature pyramid networks. In our experiments, we demonstrated how rBox helps to improve the performance of object detection compared with traditional bounding boxes. Besides, we evaluated the performance of our DrBox family on a series of object detection tasks.

关键词： Object detection Remote sensing images Rotated object Deep learning

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Advancing Few-Shot Instance Segmentation of Military Aircraft in Remote Sensing Imagery with Large Vision Models

Advancing Few-Shot Instance Segmentation of Military Aircraf...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiyu Qi Yifan Wu Wenhui Zhang Zhan Chen Yidan Zhang Lei Wang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology (TCAT) Key Laboratory of Network Information System Technology (NIST) Key Laboratory of Technology in Geo-Spatial Information Processing and Application System

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.

关键词： Instance segmentation Training Atmospheric modeling Detectors Military aircraft Data models Proposals

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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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A novel interferogram quality assessment index based on connected area

A novel interferogram quality assessment index based on conn...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Zhang Xiaolei Lv Jun Hong Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

InSAR interferogram quality assessment is a key step for the using of interferogram map. Traditionally, the interferogram is qualitatively assessed visually and quantitatively assessed by the number of residues. However, the important structure information is hardly quantifiable. This paper presents a novel index to evaluate the quality of InSAR interferogram based on connected area. After discomposing the fringes into independent connected areas, we analyze the statistical ratio of an area to its margin. Then we use the ratio as an index to quantitatively evaluate the interferogram. In the end, the presented index is used for the filtered interferogram of popular filters, and the results fit the visual judging.

关键词： Indexes Synthetic aperture radar Remote sensing Quality assessment Noise reduction Standards Coherence

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