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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Maosheng Lina Xinglong Liua Mingcang Zhub Guoqing Zhouc Zezhong Zheng Zhanyong Hed Shuang Yua Xuefeng Yange School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Sichuan Research Institute for Eco-System Restoration & Geo-Hazard Prevention Chengdu PRC China Railway Eryuan Engineering Group Co. LTD Chengdu

Landslide is one of the major geological disasters in China, which brings huge economic losses to our people every year. However, in the field of landslide treatment, the application of machine learning is scarce. In order to fill the gap in the field of landslide treatment measures based on machine learning. Firstly, random forest classification or regression algorithm was used to train and forecast each landslide treatment measure in this paper. Accuracy (ACC) was used to test the model accuracy of classification algorithm, and Mean Absolute Error (MAE) is used to test the model accuracy of regression algorithm. Random forest classification algorithm was adopted for non-numerical measures. And random forest regression algorithm was adopted for the numerical treatment measures. Secondly, the feature importance of the random forest model was calculated to obtain the more important features of each landslide treatment measure in this paper. Based on this, an optimized random forest model was constructed, and finally the optimal random forest regression and classification algorithm model suitable for landslide treatment measures recommendation was obtained. The training data dimensions of the model were reduced from 58 dimensions to 4-10 dimensions. The experimental results showed that our model could greatly improve the accuracy.

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A Method of Using Multichannel SAR spatial Phase information for Moving Target Positioning

A Method of Using Multichannel SAR Spatial Phase Information...

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IEEE International Conference on Radar

作者： Yini Lv Lihua Zhong Xiaolan Qiu Chibiao Ding Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

Multichannel synthetic aperture radar (SAR) has the ability of high-resolution and wide-swath (HRWS) imaging. Therefore, it has become a vital means of marine surveillance. Moreover, the target positioning using SAR is essential in ship monitoring, and it is mainly concentrated on the image domain at present. This paper proposes a novel method of positioning the target using the spatial phase information of SAR echo without imaging. The multichannel SAR antenna can be regarded as an array composed of multiple elements, and based on the array signal processing theory, the target cone angle at the imaging moment can be obtained. Then, according to the range-Doppler equation and the geometric model of the earth, the target coordinates can be calculated with the cone angle known. Finally, the results of the simulation experiment illustrate the effectiveness of the proposed method.

关键词： Earth Array signal processing Surveillance geometric modeling Imaging Signal processing algorithms Radar imaging

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Examining the Fourier Spectrum of Speech Signal from a Time-Frequency Perspective for Automatic Depression Level Prediction

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IEEE Transactions on Affective Computing 2025年

作者： Niu, Mingyue Tao, Jianhua He, Yongjun Zhang, Shiqing Li, Ming YanShan University School of Information Science and Engineering Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Tsinghua University Department of Automation Bejing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Taizhou University Institute of Intelligent Information Processing Taizhou318000 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using convolutional neural networks or multilayer perceptrons. Therefore, they ignore the complex element composition and time-frequency attributes of Fourier spectra, which is not conducive to capturing the differences among individuals with different depression levels. For this reason, we construct a Time-Frequency Self-Embedding (TFSE) module, which not only stores the correlation relationship among real (imaginary) parts of Fourier spectra of different subjects from the time-frequency perspective, but also maintain the physical properties of data through the weight embedding process. Besides, Global Average Pooling (GAP) or linear layers are difficult to balance both temporal and frequency dimensions in the vectorization process. Therefore, we construct a Time-Frequency Tensor Vectorization (TFTV) module, which summarizes each channel along time and frequency dimensions, and then generates the vectorization result by integrating various channels. In this way, we combine TFSE and TFTV modules to form our SpectrumFormer model for predicting depression levels. Evaluation indicators on AVEC 2013 and AVEC 2014 depression databases imply the progressiveness of our model. © 2010-2012 IEEE.

关键词： Depression level prediction Real and imaginary parts Tensor vectorization Time-frequency attributes

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MF-JMoDL-Net: A Deep Network for Azimuth Undersampling Pattern Design and Ambiguity Suppression for Sparse SAR Imaging

arXiv

引用

arXiv 2023年

作者： Wu, Yuwei Zhang, Zhe Qiu, Xiaolan Zhao, Yao Yu, Weidong Department of Space Microwave Remote Sensing System Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Jiangsu Suzhou China Suzhou Aerospace Information Research Institute Jiangsu Suzhou China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Guangdong University of Technology Guangdong Guangzhou China

Traditionally, the range swath of a synthetic aperture radar (SAR) system is constrained by its pulse repetition frequency (PRF). Given the system complexity and resource constraints, it is often difficult to achieve high imaging performance and low ambiguity without compromising the swath. In this paper, we propose a joint optimization framework for sparse strip SAR imaging algorithms and azimuth undersampling patterns based on a deep convolutional neural network, combined with matched filter (MF) approximate measurement operators and inverse MF operators, referred to as MF-JMoDL-Net, for sparse SAR imaging methods. Compared with conventional sparse SAR imaging, MF-JMoDL-Net enables us to alleviate the limitations imposed by PRF. In the proposed scheme, joint and continuous optimization of azimuth undersampling patterns and convolutional neural network parameters are implemented to suppress azimuth ambiguity and enhance sparse SAR imaging quality. Experiments and comparisons under various conditions demonstrate the effectiveness and superiority of the proposed framework in imaging results. Copyright © 2023, The Authors. All rights reserved.

关键词： Matched filters

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An adaptive Enhanced Imaging Method for Multi-aspect SAR

An adaptive Enhanced Imaging Method for Multi-aspect SAR

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IEEE International Conference on Radar

作者： Fei Teng Yun Lin Xiaoyang Yue Shanshan Feng Wen Hong Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Aerospace Information Research Institute Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9781665468893

Compared with traditional SAR working modes, multi-aspect SAR can provide images with higher resolution and signal-to-noise ratio (SNR) due to its larger synthetic aperture. However, the SNR does not increase with the increase of the aperture length. This is because the scattering is no longer isotropic as traditional SAR when the viewing angle is large. In this paper, an adaptive enhanced imaging method for multi-aspect SAR is proposed. The resolution and the SNR are maximized by scattering analysis performed simultaneously with imaging. In the process of image generation, the scattering characteristic is analyzed and all targets are divided into two categories: isotropic and anisotropic. Then different image formation strategies are used for isotropic and anisotropic target. A C-band circular SAR data is used to validate our method.

关键词： Image resolution C-band Scattering Imaging Radar scattering Radar imaging Apertures

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Characterizing key Foliar Functional Traits of Subtropical Evergreen Forests in South China Using Leaf and Uav-Based Spectroscopy

SSRN

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SSRN 2023年

作者： Wang, Zhihui Sun, Zhongyu Liu, Nanfeng Geng, Shoubao Wen, Meili Zhang, Hui Yang, Long Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-Sen University Guangzhou510275 China College of Forestry Wuzhishan National Long-Term Forest Ecosystem Monitoring Research Station Hainan University Haikou570228 China Ministry of Education College of Forestry Hainan University Haikou570228 China

Spectroscopy provides an efficient way of predicting foliar functional traits at both leaf and canopy scales. Previous studies mainly used hyperspectral data from airborne and spaceborne platforms, and few studies assessed the feasibility of the more flexible unmanned airborne vehicle (UAV) -based imaging spectroscopy in subtropical evergreen forests. This study aims to characterize key foliar traits of subtropical evergreen forests in South China using leaf and UAV-based spectroscopy. Dry and fresh leaf spectra as well as key foliar traits (leaf chlorophyll and carotenoids, water, dry matter, nitrogen, phosphorus, and potassium) were collected from 360 samples over 97 species across four field sites. For the site Heshan, we also collected UAV-based hyperspectral imageries at two dates. Partial least squares regression (PLSR) and random forest regression (RF) were used to link foliar traits with dry leaf spectra, fresh leaf spectra and canopy spectra, respectively. Cross-site and cross-season validations were performed to assess model transferability. Results showed that dry and fresh leaf spectra could estimate most foliar traits with comparable accuracies (normalized RMSE (NRMSE) © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

Stereo Matching of VHR Remote Sensing Images via Bidirection...

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IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Rongshu Tao Yuming Xiang Hongjian You Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.

关键词： Estimation Remote sensing Optical sensors Optical imaging Three-dimensional displays Buildings Satellites

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Improving resistance to adversarial deformations by regularizing gradients

arXiv

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arXiv 2020年

作者： Xia, Pengfei Li, Bin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Department of Electronic Engineering and Information Science University of Science of Technology of China Hefei China

Improving the resistance of deep neural networks against adversarial attacks is important for deploying models to realistic applications. Currently, most defense methods are designed to defend against additive noise attacks, their performance cannot be guaranteed when against non-additive noise attacks. In this paper, we focus on adversarial deformations, a typical class of non-additive noise attacks, and propose a flow gradient regularization with random start to improve the resistance of models. Theoretically, we prove that, compared with input gradient regularization, regularizing flow gradients is able to get a tighter bound. Across multiple datasets, architectures, and adversarial deformations, our experimental results consistently indicate that models trained with flow gradient regularization can acquire a better resistance than trained with input gradient regularization with a large margin. Moreover, compared with adversarial training, our method can achieve better results in optimization-based and gradient-free attacks, and combining these two methods can improve the resistance against deformation attacks further. Finally, we give a unified form of gradient regularization, which can be used to derive the corresponding form when facing other types of attack. Copyright © 2020, The Authors. All rights reserved.

关键词： Deformation

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Sequential reinforced 360-degree video adaptive streaming with cross-user attentive network

arXiv

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arXiv 2020年

作者： Fu, Jun Chen, Zhibo Chen, Xiaoming Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Beijing Technology and Business University China

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Phase Transition Analysis for Compressive Sensing Based DoA Estimation in Automotive Radar

Phase Transition Analysis for Compressive Sensing Based DoA ...

引用

International Radar Symposium (IRS)

作者： Yan Zhang Silin Gao Mingqian Liu Bingchen Zhang Yirong Wu Chinese Academy of Sciences Aerospace Information Research Institute Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Processing Application System Beijing China

ISBN: (数字)9788394942151

ISBN: (纸本)9781728157863

In automotive radar applications, the compressive sensing (CS) based DoA estimation is used in array signal processing in recent years. Sparse reconstruction has the potential to estimate the direction of arrival (DoA) with super resolution. However, failed results may be acquired via sparse reconstruction in inappropriate conditions, namely the critical condition determining success or failure must be taken into consideration. In this paper, the sparsity of the scenario and the signal-to-noise ratio (SNR) are analyzed as the main factors via phase transition diagrams. Other factors affecting the success or failure are also investigated, such as the array configuration and the sparse recovery algorithm. Simulated and experimental results demonstrate the critical conditions, in which the DoA estimation is successful or failed.

关键词： Direction-of-arrival estimation Estimation Radar applications Compressed sensing Signal resolution Signal to noise ratio Automotive engineering

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