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Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

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Journal of Arid Land 2013年第3期5卷 340-353页

作者： Fei WANG Xi CHEN GePing LUO JianLi DING XianFeng CHEN State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of Sciences University of Chinese Academy of Sciences College of Resources and Environmental Science Xinjiang University Slippery Rock University of Pennsylvania Slippery RockPA16057USA

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively pr

关键词： soil salinity spectrum halophytes Landsat TM spectral mixture analysis feature space model

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Research of Underwater Bottom Object and Reverberation in feature space

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Journal of Marine Science and Application 2013年第2期12卷 235-239页

作者： Xiukun Li Zhi Xia Science and Technology on Underwater Acoustic Laboratory Harbin Engineering University Harbin 150001 China

The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.

关键词： underwater bottom object pattern of reverberation feature clustering feature space underwater object detection

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Multi-Distributed Speech Emotion Recognition Based on Mel Frequency Cepstogram and Parameter Transfer

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Chinese Journal of Electronics 2022年第1期31卷 155-167页

作者： LIN Long TAN Liang School of Computer Science Sichuan Normal University Institute of Computing Technology Chinese Academy of Sciences

Speech emotion recognition(SER) is the use of speech signals to estimate the state of emotion. At present, machine learning is one of the main research methods of SER, the test and training data S of traditional machine learning all have the same distribution and feature space, but the data of speech is accessed from different environments and devices, with different distribution characteristics in real life. Thus, the traditional machine learning method is applied to the poor performance of SER. This paper proposes a multi-distributed SER method based on Mel frequency cepstogram(MFCC) and parameter transfer. The method is based on single-layer long short-term memory(LSTM), pre-trained inceptionv3 network and multi-distribution corpus. The speech pre-processed MFCC is taken as the input of single-layer LSTM, and input to the pre-trained inception-v3 *** features are extracted through the pre-trained inception-v3 model. Then the features are sent to the newly defined the fully connected layer and classification layer,let the parameters of the fully connected layer be finetuned, finally get the classification result. The experiment proves that the method can effectively complete the classification of multi-distribution speech emotions and is more effective than the traditional machine learning framework of SER.

关键词： multidistribution corpus fully connected layer feature extraction speech signals machine learning method pre-trained inception-v3 model multidistributed speech emotion recognition pre-trained inception-v3 network feature space recurrent neural nets Mel frequency cepstogram parameter transfer single-layer LSTM speech pre-processed MFCC learning (artificial intelligence) speech recognition emotion recognition multidistributed SER method multidistribution speech emotions

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Structure-Aware Nonlocal Optimization Framework for Image Colorization

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Journal of Computer Science & Technology 2015年第3期30卷 478-488页

作者：赵汉理聂桂芝厉旭杰金小刚潘志庚 Intelligent Information Systems Institute Wenzhou University Wenzhou 325035 China State Key Lab of CAD & CG Zhejiang University Hangzhou 310058 China Digital Media and HCI Research Center Hangzhou Normal University Hangzhou 311121 China

This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colo- rization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous regions and remote texture-similar regions without explicit image segmentation. We implement the nonlocal principle by computing k nearest neighbors in the high-dimensional feature space. The feature space contains not only image coordinates and intensities but also statistical texture features obtained with the direction-aligned Gabor wavelet filter. Structure maps are utilized to scale texture features to avoid artifacts along high-contrast boundaries. We show various experimental results and comparisons on image colorization, selective recoloring and decoloring, and progressive color editing to demonstrate the effectiveness of the proposed approach.

关键词： colorization recoloring feature space relative total variation

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Boosting Adversarial Training with Learnable Distribution

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Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

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Spatial-temporal variation and attribution of salinization in the Yellow River Basin from 2015 to 2020

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International Journal of Digital Earth 2023年第1期16卷 446-463页

作者： Hong Mengmeng Wang Juanle Han Baomin State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China Shandong University of Technology ZiboPeople’s Republic of China Jiangsu Collaborative Innovation Center for Geographic Information Resources Development and Utilization NanjingPeople’s Republic of China

Under the pressure of SDG15.3.1 compliance,it is imperative to solve the land salinization degradation problem in the Yellow River Basin as China’s *** the view of geographical scale,six zoning units were divided in the Yellow River Basin with‘climate-meteorology-geomorphology’as the main controlling factor,and a salinization inversion model was constructed for each zoning *** surface parameters were selected to construct a three-dimensional feature space according to the individual geographical *** on the cloud data processing capability of the Google Earth Engine platform,a feature space inversion process was applied for automatic inversion of *** distribution maps of the Yellow River Basin in 2015 and 2020 were obtained at 30 m resolution by classifying the salinization inversion *** distribution and spatiotemporal variation of salinization as well as the causes of salinization were *** prevention and control suggestions were subsequently *** study could also be scaled up to larger and more complex geographical regions.

关键词： Salinization land degradation Yellow River Basin Google Earth Engine feature space sustainable development goals 15

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feature Representations Using the Reflected Rectified Linear Unit(RReLU) Activation

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Big Data Mining and Analytics 2020年第2期3卷 102-120页

作者： Chaity Banerjee Tathagata Mukherjee Eduardo Pasiliao Jr. Department of Idustrial&Systems Engineering University of Central FloridaFloridaOrlandoFL 32816-2368USA Department of Computer Science University of Alabama in HuntsvilleHuntsvilleHuntsvilleAL 35806USA Air Force Research Labs United States Air ForceEglin Air Force BaseShalimarFL 32579USA

Deep Neural Networks(DNNs)have become the tool of choice for machine learning practitioners *** important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different *** this work,we introduce a four-output activation function called the Reflected Rectified Linear Unit(RRe LU)activation which considers both a feature and its negation during *** activation function is"sparse",in that only two of the four possible outputs are active at a given *** test our activation function on the standard MNIST and CIFAR-10 datasets,which are classification problems,as well as on a novel Computational Fluid Dynamics(CFD)dataset which is posed as a regression *** the baseline network for the MNIST dataset,having two hidden layers,our activation function improves the validation accuracy from 0.09 to 0.97 compared to the well-known Re LU *** the CIFAR-10 dataset,we use a deep baseline network that achieves 0.78 validation accuracy with 20 epochs but overfits the *** the RRe LU activation,we can achieve the same accuracy without overfitting the *** the CFD dataset,we show that the RRe LU activation can reduce the number of epochs from 100(using Re LU)to 10 while obtaining the same levels of performance.

关键词： deep learning feature space approximations multi-output activations Rectified Linear Unit(ReLU)

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Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm

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Journal of Beijing Institute of Technology 2004年第4期13卷 351-354页

作者：李侃刘玉树 School of Information Science and Technology Beijing Institute of Technology Beijing100081 China

A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.

关键词： fuzzy c-means mercer kernel feature space validity measure function

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Spatial and Temporal Pattern Changes and Driving Forces:Analysis of Salinization in the Yellow River Delta from 2015 to 2020

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Journal of Resources and Ecology 2022年第5期13卷 786-796页

作者： HONG Mengmeng WANG Juanle HAN Baomin School of Civil and Architectural Engineering Shandong University of TechnologyShandong 255049China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

China’s Yellow River Delta represents a typical area with moist semi-humid soil salinization,and its salinization has seriously affected the sustainable use of local *** use of remote sensing technology to understand changes in the spatial and temporal patterns of salinization is key to combating regional land *** this study,a feature space model was constructed for remote sensing and monitoring land salinization using Landsat 8 OIL multi-spectral *** feature parameters were paired to construct a feature space model;a total of eight feature space models were *** accuracy analysis was conducted by combining salt-loving vegetation data with measured data,and the model demonstrating the highest accuracy was selected to develop salinization inversion maps for 2015 and *** results showed that:(1)The total salinization area of the Yellow River Delta displayed a slight upward trend,increasing from 4244 km^(2) in 2015 to 4629 km^(2) in ***,the area’s salting degree reduced substantially,and the areas of saline soil and severe salinization were reduced in size;(2)The areas with reduced salinization severity were mainly concentrated in areas surrounding cities,and primarily comprised wetlands and some regions around the Bohai Sea;(3)Numerous factors such as the implementation of the“Bohai Granary”cultivation engagement plan,increase in human activities to greening local residential living environments,and seawater intrusion caused by the reduction of sediment contents have impacted the distribution of salinization areas in the Yellow River Delta;(4)The characteristic space method of salinization monitoring has better applicability and can be promoted in humid-sub humid regions.

关键词： salinization land degradation feature space salt-loving vegetation the Yellow River Delta

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Pri-EMO:A universal perturbation method for privacy preserving facial emotion recognition

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Journal of Information and Intelligence 2023年第4期1卷 330-340页

作者： Yong Zeng Zhenyu Zhang Jiale Liu Jianfeng Ma Zhihong Liu School of Cyber Engineering Xidian UniversityXi'an 710071China

Facial emotion have great significance in human-computer interaction,virtual reality and people's *** methods for facial emotion privacy mainly concentrate on the perturbation of facial emotion ***,cryptography-based perturbation algorithms are highly computationally expensive,and transformation-based perturbation algorithms only target specific recognition *** this paper,we propose a universal feature vector-based privacy-preserving perturbation algorithm for facial *** method implements privacy-preserving facial emotion images on the feature space by computing tiny perturbations and adding them to the original *** addition,the proposed algorithm can also enable expression images to be recognized as specific *** show that the protection success rate of our method is above 95%and the image quality evaluation degrades no more than *** quantitative and qualitative results show that our proposed method has a balance between privacy and usability.

关键词： Facial emotion recognition Privacy preserving Perturbation Universal algorithm feature space

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