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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing"

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Hyperspectral remote sensing image classification based on kernel fisher discriminant analysis

Hyperspectral remote sensing image classification based on k...

引用

International conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Guo-Peng Yang Hang-Ye Liu Xu-Chu Yu Institute of Surveying and Mapping Zhengzhou China

Hyperspectral remote sensing technology combines the radiation information which relates to the targets' attribute and the space information which relates to the targets' position and shape. The spectrum information, which is enriched in the hyperspectral image, can facilitate the ground target classification, comparing with panchromatic remote sensing image and the multispectral remote sensing image. This paper introduced the classification method based on the kernel Fisher discriminant analysis, and then researched the selection methods of the kernel function and its parameter, and studied the decomposition methods on multi-classes classification methods. We selected the Gauss radial basic function and used the cross-validating grid search to find suitable parameters, to build an effective and robust multi-classes KFDA classifier. And then we applied this method to the hyperspectral remote sensing image classification, and the result showed that it has comparable classification accuracy in comparison to support vector machine, but the computation time is much less.

关键词： Hyperspectral sensors Hyperspectral imaging remote sensing image classification Kernel image analysis Space technology Shape Gaussian processes Robustness

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Simulation of atmospheric profile retrieval sensitivity with cloud from hyperspectral infrared data

Simulation of atmospheric profile retrieval sensitivity with...

引用

5th International Symposium on Multispectral image processing and pattern recognition

作者： Guan, Li Huang, Hung Lung Nanjing Univ Informat Sci & Technol Sch Remote Sensing Nanjing 210044 Peoples R China Univ Wisconsin Cooperat Inst Meteorol Satellite Studies Madison WI 53706 USA

ISBN: (纸本)9780819469519

In this paper the simulated space-based high spectral resolution atmospheric infrared sounder (AIRS) infrared radiances with different cloud top heights and effective cloud fractions are used to demonstrate the measurement sensitivity and atmospheric profile retrieval performance. The simulated cloudy retrieval of atmospheric temperature and moisture derived from the statistical eigenvector regression algorithm are analyzed with different effective cloud fractions and different cloud height. The temperature and humidity root-mean-square error with cloud fraction ranging from 0.1 to 1.0 (with interval of 0.1) for cloud height (200, 300, 500, 700 and 850 hPa) known perfectly and cloud height error of 50 hPa are computed. Results show that the root-mean-square error of retrieved temperature and the mixed ratio of water vapor below the cloud top increase with effective cloud fraction. The retrieval accuracy of the cloud height error of 50 hPa decrease comparing with the cloud height known perfectly, while the temperature retrieval is more sensitive to cloud height error than humidity retrieval.

关键词： infrared high spectral atmospheric infrared sounder temperature and humidity retrieval

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Air quality derivation utilizing landsat TM image over Penang, Malaysia

Air quality derivation utilizing landsat TM image over Penan...

引用

3rd International conference on Recent Advances in Space Technologies

作者： Saleh, N. Molid. Lim, H. S. MatJafri, M. Z. Abdullah, K. Univ Sains Malaysia Sch Phys Minden 11800 Penang Malaysia

ISBN: (纸本)9781424410569

This paper outlines recent developments in optical remote sensing of Landsat TM data for air quality monitoring for atmospheric particulate matter having a diameter less than 10- micro meter (PM10). The objective of this study is to evaluate the performance of the developed algorithm and suitability of remote sensing data for PM10 mapping. We used a DustTrak Aerosol Monitor 8520 to collect in situ data. The PM10 data were collected simultaneously during the satellite Landsat overpass the study area. An algorithm has been developed based on the optical aerosol characteristic in the atmosphere to estimate PM10 over Penang Island, Malaysia. The digital numbers were determined corresponding to the ground-truth locations for each band and then converted to radiance and reflectance values. The atmospheric reflectance values was extracted from the satellite observed reflectance values subtrated by the amount given by the surface reflectance. The surface refleatance values were retrieved using ATCOR2 in the PCI Geomatica 9.1 image processing software. The atmospheric reflectance values were later used for PM10 mapping using the calibrated algorithm. Results from this research have indicated that PM10 data have positive correlation with atmospheric reflectance in two visible bands (Red and Blue band). Finally, the map of pollution concentration generated from the satellite image using the proposed algorithm to illustrate spatial distribution pattern of air pollution for the study area. The proposed algorithm produced high correlation coefficient, R, and low root-mean-square, RMS, values. The concentrations of PM10 are high in the industrial zones and urban areas of Penang, Malaysia.

关键词： landsat TM PM10 aerosol

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MULTISPECTRAL remote sensing image CLASSIFICATION WITH MULTIPLE FEATURES

MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION WITH MULTI...

引用

2007 International conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： QIAN YIN PING GUO Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 Chin Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 Chin

In this paper, we propose to combine the spectral and texture features to compose the multi-feature vectors for the classification of multispectral remote sensing *** usually is difficult to obtain the higher classification accuracy if only considers one kind feature, especially for the case of different geographical objects have the same spectrum or texture specialty for a multispectral remote sensing *** spectral feature and the texture feature are composed together to form a new feature vector, which can represent the most effective features of the given remote sensing *** this way we can overcome shortcomings of only using the single feature and raise the classification *** system classification performance with composed feature vector is investigated by *** analysis of results we can learn how to combine the multi-feature vector can obtain a higher classification rate, and experiments proved that the proposed method is feasible and useful in multispectral remote sensing image classification study.

关键词： Spectrum feature Texture feature Multispectral remote sensing image Feature combination Classification.

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New feature selection method for EO-1/Hyperion image classification - a case study of Subei region, China - art. no. 67872M

New feature selection method for EO-1/Hyperion image classif...

引用

5th International Symposium on Multispectral image processing and pattern recognition

作者： Jiang, Xiaoguang Li, Xianbin Me, Huihui Fang, Bin Xi, Xiaohuan Chinese Acad Sci Acad Optoelect Beijing 100080 Peoples R China

ISBN: (纸本)9780819469519

Hyperspectral remote sensing can provide tens, even hundreds of spectral bands imagery, which helps us detect the diagnostical spectral characteristics of detected objects. However, there is relatively high correlation between different bands and much redundancy in hyperspectral data sets. Therefore, one of the most important procedures before application is to select optimal bands for extracting information from hyperspectral data effectively. In this paper, we first introduce the characteristics of EO-1/Hyperion, and apply several important pre-processing procedures to Hyperion L1R data, such as radiometric calibration, destriping, smile correction etc. Then we apply spectrum reconstruction approach to feature selection, which uses several basis functions and corresponding spectral intervals to describe the spectrum extracted from Hyperion hyperspectral data sets in Subei region, China. The feature selection method based on spectrum reconstruction is incrementally adding bands to the initial bands, followed by adjustment of band widths and locations. At last, we aggregate several Hyperion bands into a new simulated band in each interval and apply Maximum Likelihood Classification (WC) method to it. The overall accuracy of classification is 92% compared with in situ measurement, which supports the validity of this feature selection method.

关键词： hyperspectral feature selection spectrum reconstruction basis function spectral interval

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Realization of real-time edge detection of weld pool for YAG laser welding in TWB

Realization of real-time edge detection of weld pool for YAG...

引用

IEEE International conference on Automation and Logistics

作者： Zhang, Lei Zhao, Mingyang Zhao, Lihua Chinese Acad Sci Shenyang Inst Automat Grad Sch Shenyang Liaoning Peoples R China Shan Dong Second Coll Technol Dept Econom Management Liaocheng Shandong Peoples R China

ISBN: (纸本)9781424415304

Due to the disturbances of spatters, dusts and strong arc light, it is difficult to detect the weld pool edge in laser welding processes. An image sensing system for the ND: YAG laser welding process is introduced in detail. image processing and pattern recognition in the system are first used to obtain information from the laser welding process for Tailed Weld Blanks. A new way is employed to preprocess the image of the laser welding in order to detect effectively the edge of weld pool in Tailed Weld Blanks. The image of the weld pool was processed using a series of methods: image truncation, Bi-level thresholding, median filter and edge detection. The experimental results show that better performance to extract the edge of the weld pool can be obtained by using the new way. The proposed edge detection approach can reach not only perfect edge detection result but also good robustness to noise in TWB. Experiments also show that using the welding monitor system to control the ND:YAG laser welding quality for stainless steel is an effective method.

关键词： laser welding weld pool image processing edge detection

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Optical resources for highly secure remote object authentication

Optical resources for highly secure remote object authentica...

引用

conference on Applications of Digital image processing XXX

作者： Millan, Maria S. Perez-Cabre, Elisabet Javidi, Bahram Univ Politecn Cataluna Dept Opt & Optomet Terrassa Spain Univ Connecticut Elect & Comp Engn Dept 371 Fairfield RdUnit 2157 Storrs CT 06269 USA

ISBN: (纸本)9780819468444

We review the potential of optical techniques in security tasks and propose to combine some of them in automatic authentication. More specifically, we propose to combine visible and near infrared imaging, optical decryption, distortion-invariant ID tags, optoelectronic devices, coherent image processor, optical correlation, and multiple authenticators. A variety of images and signatures, including biometric and random sequences, can be combined in an optical ID tag for multifactor identification. Encryption of the information codified in the ID tag allows increasing security and deters from unauthorized usage of optical tags. The identification process encompasses several steps such as detection, information decoding and verification which are all detailed in this work. Design of rotation and scale invariant ID tags is taken into account to achieve a correct authentication even if the ID tag is captured in different positions. Resistance to some noise and degradation of the tag is analyzed. Examples and experiments are provided and the results discussed.

关键词： information security optical ID tags pattern recognition multifactor validation near infrared imaging

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Color component prediction in multispectral space based on rotated principal component analysis

Color component prediction in multispectral space based on r...

引用

5th International Symposium on Multispectral image processing and pattern recognition

作者： Xu, Faqiang Wan, Xiaoxia Zhu, Yuanhong Wuhan Univ Sch Remote Sensing Informat Engn 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Dept Printing & Packaging Wuhan 430079 Hubei Peoples R China Shenzhen Polytech Sch Media & Commun Shenzhen 518055 Guangdong Peoples R China

ISBN: (纸本)9780819469519

Aiming at the problem of negative index in the spectral color space built by means of traditional principal component analysis (PCA), a method of color component prediction based on rotated principal component analysis (RPCA) is proposed, which performs the rotating transformation from initial eigenvectors to a set of all-positive vectors as the physical basis color components while retaining the cumulative ratio of the variance contributions of significant principal components to the original multispectral space to the maximum extent. The rotated column vectors should be also polarized between 0 and 1. The spectral database of Munsell Matte Collection I is used for experiment. The experimental results show that the novel method of prediction not only uncovers the real color components of the target image better but reconstructs the normalized spectra data set with a high colorimetric and spectral accuracy. Thereinto, the colorimetric errors of the four estimated components reconstruction for more than 96 percent of the samples in Munsell Matte Collection I are less than 3 units of color difference acceptable.

关键词： color match color component prediction RPCA varimax all-positive vector polarization

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International conference on Computing: Theory and Applications-Title

International Conference on Computing: Theory and Applicatio...

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International conference on Computing: Theory and Applications, ICCTA

The following topics are dealt with: parallel and distributed computing; mobile and wireless networks; VLSI design; high performance computing; pattern recognition and soft computing; artificial neural networks; fuzzy set theory; support vector machines; data mining; document processing; natural language processing; speech and signal processing; image processing; computer vision; content based image retrieval; remote sensing

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Aerosol polarimetry sensor for the Glory Mission

Aerosol polarimetry sensor for the Glory Mission

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5th International Symposium on Multispectral image processing and pattern recognition

作者： Peralta, Richard J. Nardell, Carl Cairns, Brian Russell, Edgar E. Travis, Larry D. Mishchenko, Michael I. Fafaul, Bryan A. Hooker, Ronald J. Raytheon Santa Barbara Remote Sensing 75 Coromar Dr Goleta CA 93117 USA NASA Goddard Space Flight Ctr Washington DC USA NASA Washington DC USA

ISBN: (纸本)9780819469502

This paper describes the Glory Mission Aerosol Polarimetry Sensor (APS) being built by Raytheon under contract to NASA's Goddard Space Flight Center. Scheduled for launch in late 2008, the instrument is part of the US Climate Change Research Initiative to determine the global distribution of aerosols and clouds with sufficient accuracy and coverage to establish the aerosol effects on global climate change as well as begin a precise long-term aerosol record. The Glory APS is a polarimeter with nine solar reflectance spectral bands that measure the first three Stokes parameters vector components for a total of 27 unique measurements. In order to improve the reliability and accuracy of the measurements, additional 9 redundant measurements are made, yielding a total of 36 channels. The sensor is designed to acquire spatial, temporal, and spectral measurements simultaneously to minimize instrumental effects and provide extremely accurate Raw Data Records. The APS scans in the direction close to of the spacecraft velocity vector in order to acquire multi-angle samples for each retrieval location so that the Stokes parameters can be measured as functions of view angle.

关键词： polarization polarimetry global warming climate forcing aerosols black carbon MODIS MISR POLDER Glory Mission

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