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8th EAI International Conference on Machine Learning and Intelligent Communications, MLICOM 2023

作者： Qi, Xiyu Wu, Yifan Mao, Yongqiang Zhang, Wenhui Zhang, Yidan Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031717154

The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM’s dependency on manual guidance given its category-agnostic nature, we identified unexplored potential within few-shot semantic segmentation tasks for remote sensing imagery. This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided mask to produce coarse pixel-wise prompts for SAM. Extensive experiments on the DLRSD datasets underlines the superiority of our approach, outperforming other available few-shot methodologies. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Semantic Segmentation

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THE DUAL-ASPECT GEOMETRIC TERRAIN CORRECTION METHOD USING GF-3 SATELLITE DATA

THE DUAL-ASPECT GEOMETRIC TERRAIN CORRECTION METHOD USING GF...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Jiayin Liu Xiaolan Qiu Baoquan Zhang Feng Wang Lei Li Key Laboratory of Technology on Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Beijing China

The GF-3 satellite is the first full-polarization SAR satellite in China, which operates in the C band with a resolution of 1m. This paper presents a dual-aspect geometric terrain correction method to overcome the inherent shortages of SAR image such as foreshortening, shadow and layover. The results using GF-3 satellite data show that the method can effectively eliminate layover and shadow distortions in SAR images. This method solves the geometric correction problem that cannot be solved with a single SAR image.

关键词： Satellites Radar polarimetry Synthetic aperture radar Mathematical model Orbits Image resolution Satellite broadcasting

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Nonparametric statistics inspired similarity measure for accuracy in collaborative filtering

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Journal of Computational information systems 2013年第20期9卷 8397-8406页

作者： Liu, Shichen Xiong, Yan Liu, Qingwen Qi, Xiang Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China

Similarity computation is especially significant in collaborative filtering algorithms. In the existed literatures or large recommender systems, researchers generally use cosine similarity or Pearson correlation coefficient to compute the similarities. However, on one hand, both two measures are more applicable in linear space while the ratings cannot linearly reflect users' interest intensity. On the other hand, different systems have different rating restricts, so it's unreasonable to treat varied systems with the same computation method. In this work, inspired by nonparametric statistics, a sound memory-based method with a novel similarity measure is proposed to eliminate the affection of rating distributions and system diversities on similarity computation. Experiments based on Jester are set to evaluate the performance of the approach. And the highly competitive results show that our approach is able to significantly improve the recommendation quality. © 2013 Binary information Press.

关键词： Collaborative filtering

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A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELECTRIC PERIODIC STRUCTURES

A DESIGN OF THE COMPLETE POLARIZATION CONVERTER USING DIELEC...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yueting Zhang Chibiao Ding Bin Lei Weihai Fang Key Laboratory of Geo-spatial Information Processing and Application System Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing General Institute of Electronic Engineering

Polarization converter is used in the applications of the polar SAR observations. There exists coupling between TE and TM modes when plane wave is oblique incident on the surface of dielectric periodic structure, the single TE or TM polarized wave incident will cause TE and TM mixed transmission wave. In some proper incident conditions, complete polarization conversion can be realized between TE and TM mode. In this work, a design of complete polarization converter by using dielectric periodic structure is designed and it is carefully investigated by a method which combines the multimode network theory with the rigorous mode matching method. We revealed TE/TM complete polarization conversion characteristics of dielectric periodic structure, and also analyzed the effects of structure parameters. These investigations provide important guideline for accurate designing new millimeter wave polarization converters.

关键词： Dielectrics Polarization Periodic structures Optical waveguides Microwave filters Reflection

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1-km/Daily Land Surface Temperature Optimized Dataset for the Qinghai-Tibet Plateau Based on MODIS Data(2000-2020)

全球变化数据仓储（中英文）

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全球变化数据仓储（中英文） 2023年第10期 4-7页

作者： XU Xunpeng ZHANG Yu JI Luyan TANG Hairong Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China the School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 101408China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China the Key Laboratory of Technology in Geo-Spatial information Processing and Application System Chinese Academy of SciencesBeijing 100190China

Remote sensing data has strong correlation and continuity in space and time,so time series remote sensing images have low-rank *** this dataset,we repaired images using low-rank tensor ***,we preprocessed the MODIS land surface temperature data and employed spatio-temporal interpolation to initially fill in the missing values caused by cloud ***,we treated the land surface temperature time series data as a third-order spatio-temporal tensor and introduced Fourier transform on the time dimension to convert it into a space-frequency *** performing singular value decomposition and Gaussian low-pass filtering on this tensor,followed by inverse Fourier transform,we obtained a space-time ***,we further optimized the missing tensor using the alternating direction method of *** data accuracy using the method was validated through simulation experiments,where artificial masks were added and subsequently *** resulting mean absolute error(MAE)falls within the range of 2.1℃to 4.9℃.This dataset includes the following data for the Tibetan Plateau on a daily basis for the years 2000-2020:(1)the optimized surface temperature data for the cloud-shaded regions of the MOD11A1,MYD11A1 products(MOD11A1_QTP_PART,MYD11A1_QTP_PART);(2)optimized MOD11A1/MYD11A1 data(MOD11A1_QTP_TEMP,MYD11A1_QTP_TEMP);and(3)original MOD11A1 and MYD11A1 products(MOD11A1_QTP_ORIGIN,MOD11A1_QTP_ORIGIN).All data have a spatial resolution of 1 km and are stored in an integer data format,with pixel value representing the thermodynamic temperature of the surface with a scale factor of 0.02 in *** dataset is archived *** format,and consists of 43833 data files with data size of 143 GB(compressed into 21 files with 138 GB).

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DEM reconstruction of mountainous area from two anti-parallel aspects of airborne InSAR data

DEM reconstruction of mountainous area from two anti-paralle...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fangfang Li Donghui Hu Jiayin Liu Xiaolan Qiu Chibiao Ding Institute of Electronics Chinese Academy of Sciences Beijing China Key Laboratory of Geo-Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China

The geometry distortion phenomenon of SAR imaging arises in the mountainous scenarios due to the presence of strong terrain slopes. Because of phase discontinuities or the absence of valid phase, for single pass interferometric SAR (InSAR), it is difficult to recover accurate digital elevation model (DEM) in such areas. Fusion of two or more different aspects of InSAR data is practicable to deal with this problem. In this paper, the processing procedures of airborne InSAR data are presented. In order to decrease the processing error of every single aspect data, an iterative motion compensation (MOCO) method is used. Besides, the interferometric phase of shadow area is linearly complemented before phase unwrapping to avoid error spreading. Experimental results using two anti-parallel aspects of airborne InSAR data validate the feasibility of fusion.

关键词： Geometry Motion compensation Iterative methods Surfaces Aerospace electronics Radar polarimetry

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Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks

arXiv

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arXiv 2024年

作者： Li, Ziqiang Zeng, Yueqi Xia, Pengfei Liu, Lei Fu, Zhangjie Li, Bin Nanjing University of Information Science and Technology China Big Data and Decision Lab University of Science and Technology of China China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

With the burgeoning advancements in the field of natural language processing (NLP), the demand for training data has increased significantly. To save costs, it has become common for users and businesses to outsource the labor-intensive task of data collection to third-party entities. Unfortunately, recent research has unveiled the inherent risk associated with this practice, particularly in exposing NLP systems to potential backdoor attacks. Specifically, these attacks enable malicious control over the behavior of a trained model by poisoning a small portion of the training data. Unlike backdoor attacks in computer vision, textual backdoor attacks impose stringent requirements for attack stealthiness. However, existing attack methods meet significant trade-off between effectiveness and stealthiness, largely due to the high information entropy inherent in textual data. In this paper, we introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs). Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection. Through the integration of these techniques, EST-Bad demonstrates an efficient achievement of competitive attack performance while maintaining superior stealthiness compared to prior methods across various text classifier datasets. © 2024, CC BY.

关键词： Deep neural networks

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The Research on the Space-Time Variation of Phase Imbalance for GF-3 Azimuth Multichannel Mode

The Research on the Space-Time Variation of Phase Imbalance ...

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Asian and Pacific Conference on Synthetic Aperture Radar (APSAR)

作者： Mingyang Shang Xiaolan Qiu Bing Han Lihua Zhong Chibiao Ding Junying Yang 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

Azimuth multichannel (AMC) synthetic aperture radar (SAR) is an advanced technique which can prevent the minimum antenna area constraint and provide high-resolution and wide-swath (HRWS) SAR images. Channel imbalance calibration is an important topic in AMC SAR signal processing, since it has a great impact on the image quality. The phase imbalance of AMC SAR is not only related to the radar system, but also to the satellite attitude. This paper first models the relationship between phase imbalance and satellite attitude, and analyzes the influence of satellite attitude on the phase imbalance. In the presence of the satellite attitude deviation, the phase imbalance is time-variation in azimuth, and spatial-variation in range. Then, the real data acquired by Gaofen-3 (GF-3) in the ultra-fine strip-map (UFS) mode is used to verify the relationship model both in theoretical calculations and estimation. The result shows that the model proposed in this paper can describe the space-time variation of phase imbalance very accurately. Finally, some discussions and conclusions are drawn and the future research perspectives are proposed.

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded ...

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2022 International Conference on Radar systems, RADAR 2022

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

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A FEATURE ENHANCEMENT METHOD BASED ON THE SUB-APERTURE DECOMPOSITION FOR ROTATING FRAME SHIP DETECTION IN SAR IMAGES

A FEATURE ENHANCEMENT METHOD BASED ON THE SUB-APERTURE DECOM...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Lei, Songlin Qiu, Xiaolan Ding, Chibiao Lei, Shujie National Key Laboratory of Microwave Imaging Technology Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Radio Equipment Research Institute Shanghai201100 China

ISBN: (纸本)9781665403696

Deep learning algorithms are widely used in SAR target detection. At present, most detection methods based on neural networks treat SAR images as optical images for processing, and do not fully exploit the characteristics of SAR images. In this paper, a method for feature enhancement using sub-aperture decomposition is proposed. Considering the advantages of the rotate box in eliminating interference, we conducted experiments on the rotating frame detection network Rotate RetinaNet using the Complex SAR images Rotation Ship Detection Dataset (CSRSDD). Compared with the base method, AP improves by 2.4% without bells and whistles. The results confirm the effectiveness of the proposed method. © 2021 IEEE

关键词： Neural networks Optical computing Object detection Optical fiber networks Optical detectors Optical imaging Radar polarimetry

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