2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第6页

Context-Dependent Fusion for mine detection using Airborne Hyperspectral Imagery 基于上下文的机载高光谱图像地雷探测融合

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288973

Lijun Zhang, H. Frigui, P. Gader, Jeremy Bolton

引用次数: 6

On the reliability of PCA for complex hyperspectral data 复杂高光谱数据主成分分析的可靠性

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289076

P. Bajorski

{"title":"On the reliability of PCA for complex hyperspectral data","authors":"P. Bajorski","doi":"10.1109/WHISPERS.2009.5289076","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289076","url":null,"abstract":"Principal Component Analysis (PCA) is a popular tool for initial investigation of hyperspectral image data. There are many ways in which the estimated eigenvalues and eigenvectors of the covariance matrix are used. Further steps in the analysis or model building for hyperspectral images are often dependent on those estimated quantities. It is therefore important to know how precisely the eigenvalues and eigenvectors are estimated, and how the precision depends on the sampling scheme, the sample size, and the covariance structure of the data. This issue is especially relevant for applications such as difficult target detection, where the precision of further steps in the algorithm may depend on the reliable knowledge of the estimated eigenvalues and eigenvectors. The sampling properties of eigenvalues and eigenvectors are known to some extent in statistical literature (mostly in the form of asymptotic results for large sample sizes). Unfortunately, those results usually do not apply in the context of hyperspectral images. In this paper, we investigate the sampling properties of eigenvalues and eigenvectors under three scenarios. The first two scenarios consider the type of sampling traditionally used in statistics, and the third scenario considers the variability due to image noise, which is more appropriate for hyperspectral imaging applications. For all three scenarios, we show the precision associated with the estimated eigenvalues and eigenvectors.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"01 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Hyperspectral imaging for mushroom (agaricus bisporus) quality monitoring 双孢蘑菇品质监测的高光谱成像技术

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289074

A. Gowen, C. O’Donnell, J. Frías, G. Downey

引用次数: 6

Accurate SVM classification using border training patterns 使用边界训练模式的准确SVM分类

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289110

B. Demir, S. Ertürk

引用次数: 3

On the evaluation of synthetic hyperspectral imagery 关于合成高光谱图像的评价

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289077

M. Mendenhall, E. Merényi

引用次数: 4

Feature selection and broad band bitumen content estimation of Athabasca oil sand from infrared reflectance spectra 基于红外反射光谱的阿萨巴斯卡油砂特征选择及宽带沥青含量估算

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289042

Jilu Feng, B. Rivard, A. Gallie, E. Cloutis

引用次数: 5

Variation and sensitivity in spectral thermal IR emissivity measurements 光谱热红外发射率测量的变化和灵敏度

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289079

L. Balick, Michael Howard, Heather Gledhill, A. Klawitter, A. Gillespie

{"title":"Variation and sensitivity in spectral thermal IR emissivity measurements","authors":"L. Balick, Michael Howard, Heather Gledhill, A. Klawitter, A. Gillespie","doi":"10.1109/WHISPERS.2009.5289079","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289079","url":null,"abstract":"Changes of spectra due to changes of variations in the physical features of the surface and view angle can impact the processing and exploitation of thermal IR hyperspectral images and need to be understood. To help accomplish this, FTIR spectrometer measurements were made on a set of known targets over a period of three days in order to assess the precision and repeatability of thermal IR spectral emissivity measurements of the effects of view direction and surface roughness. These measurements are intended to provide validation data for the model and advance our capability to make precise measurements of emissivity in the field. Two types of rock targets, each with three roughness classes and at three view nadir angles were measured over three consecutive days. The results within days were very consistent. Emissivity for the 60 degree view was shifted downward: a sensitivity analysis was conducted to explain this and other variations and is discussed in this paper. The spectra for the large rock size class tended to be lower than targets with smaller rocks. Preliminary modeling results will be presented.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133298364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Algorithms for robust signal subspace identification in hyperspectral images: A comparative analysis 高光谱图像中鲁棒信号子空间识别算法:比较分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288995

N. Acito, G. Corsini, M. Diani

引用次数: 0

Hyperspactral data analysis of nitrogen fertilization effects on winter wheat using spectrometer in North China Plain 华北平原冬小麦施氮效应的超空间光谱分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289007

M. Gnyp, Fei Li, Y. Miao, W. Koppe, L. Jia, Xin-ping Chen, Fusuo Zhang, G. Bareth

引用次数: 2

Discrimination of fungal disease infestation in oil-palm canopy hyperspectral reflectance data 油棕冠层高光谱反射数据中真菌侵染的判别

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289017

C. Lelong, J. Roger, Simon Brégand, Fabrice Dubertret, Mathieu Lanore, Nurul A. Sitorus, Doni A. Raharjo, J. Caliman

引用次数: 6