2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第2页

Improving the quality of extracted endmembers 提高提取端元的质量

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289104

Q. Du, Liangpei Zhang, N. Raksuntorn

引用次数: 7

A nonparametric contextual classification based on Markov random fields 基于马尔可夫随机场的非参数上下文分类

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288978

Bor-Chen Kuo, Chun-Hsiang Chuang, Chih-Sheng Huang, C. Hung

引用次数: 12

Multiple endmember spectral-angle-mapper (sam) analysis improves discrimination of savanna tree species 多端元光谱角映射器(sam)分析提高了热带草原树种的识别能力

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289031

M. Cho, R. Mathieu, P. Debba

{"title":"Multiple endmember spectral-angle-mapper (sam) analysis improves discrimination of savanna tree species","authors":"M. Cho, R. Mathieu, P. Debba","doi":"10.1109/WHISPERS.2009.5289031","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289031","url":null,"abstract":"Differences in within-species phenology and structure driven by factors including topography, edaphic properties, and climatic variables across the landscape present important challenges to species differentiation with remote sensing. The objective of this paper was to evaluate the classification performance of a multipleendmember spectral angle mapper (SAM) classification approach in discriminating seven common African savanna tree species and to compare the results with the traditional SAM classifier based on a single endmember per species or class. The leaf spectral reflectances of seven common tree species in the Kruger National Park, South Africa, Combretum apiculatum, Combretum hereroense, Combretum zeyheri, Gymnosporia buxifolia, Gymnosporia senegalensis, Lonchocarpus capassa and Terminalia sericea were used in this study. Discriminating species using all training spectra for each species as reference endmembers (i.e. the multiple endmember approach or more conventionally termed Knearest neighbour classifier) yielded a higher classification accuracy of 60% compared to the conventional SAM classifier based on the mean of the training spectra for each species (overall accuracy = 44%). Further analysis using endmembers selected after cluster analysis of all the spectra for each species yielded the highest classification accuracy for the species (overall accuracy = 74%). This study underscores two important phenomena; (i) within-species spectral variability affects the discrimination of savanna tree species with the SAM classifier and (ii) the effect of within-species spectral variability can be minimised by adopting a multiple endmember approach with the SAM classifier. This study further highlights the importance of the quality of the reference endmember or spectral library.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"10 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":"121771259","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: 16

Autoassociative neural networks for features reduction of hyperspectral data 高光谱数据特征约简的自关联神经网络

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288997

F. Frate, G. Licciardi, R. Duca

引用次数: 14

Weak signal detection in hyperspectral imagery using sparse matrix transform (smt) covariance estimation 基于稀疏矩阵变换协方差估计的高光谱图像弱信号检测

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289043

G. Cao, C. Bouman, J. Theiler

引用次数: 18

Reducing noise in hyperspectal data — A nonlinear data series analysis approach 降低高光谱数据中的噪声。非线性数据序列分析方法

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289014

D. Goodenough, T. Han

{"title":"Reducing noise in hyperspectal data — A nonlinear data series analysis approach","authors":"D. Goodenough, T. Han","doi":"10.1109/WHISPERS.2009.5289014","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289014","url":null,"abstract":"Hyperspectral data are subject to a variety of noise sources associated with the physical processes involved during data acquisition, which distort signal statistical properties and limit the applications of hyperspectral data for information extraction. Noise reduction is, therefore, a prerequisite for many hyperspectral data applications based on classification, target identification, and spectral unmixing. Studies have found that hyperspectral data are more complicated than realizations of linear stochastic processes, upon which many hyperspectral noise reduction algorithms are based. The noise in hyperspectral data may be non-Gaussian and signal dependent. Moreover, as demoustrated in our previous work, hyperspectral data exhibit apparent nonlinear characteristics, which suggests that the noise may exist in broad-band in the frequency domain. An algorithm is introduced in this paper with the intention to improve the noise reduction for hyperspectral data. The effectiveness of the algorithm is evaluated using multiple metrics focusing on both noise reduction and spectral shape preservation.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"8 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":"131683221","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: 11

Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level 在叶片水平上识别草原树木的最优个体监督高光谱波段选择

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289068

P. Debba, M. Cho, R. Mathieu

引用次数: 3

Atmospheric and topographic corrections for hyperspectral imagery 高光谱图像的大气和地形校正

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289098

V. Achard, X. Lenot

引用次数: 2

Influence of mineral (prefered) orientation on composition mapping: Observed in IR range of transmission spectra 矿物(首选)取向对成分测绘的影响:在红外透射光谱范围内观察

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289011

E. Carmina, Carrere Veronique

引用次数: 2

Change detection using synthetic hyperspectral imagery 利用合成高光谱图像进行变化检测

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289016

Karmon Vongsy, M. Mendenhall, Philip M. Hanna, Jason R. Kaufman

{"title":"Change detection using synthetic hyperspectral imagery","authors":"Karmon Vongsy, M. Mendenhall, Philip M. Hanna, Jason R. Kaufman","doi":"10.1109/WHISPERS.2009.5289016","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289016","url":null,"abstract":"In the best of circumstances, change detection (CD) is accomplished using measurements from the same instrument and under similar collection circumstances. Complications in the CD process arise when the variability in the collection process is not minimized. Variations between collected images and a lack of precise corresponding ground truth make accurate evaluation of a given CD method imprecise at best. This work leverages synthetic hyperspectral imagery, with known ground truth to include primary and tertiary materials, to investigate the use of common CD algorithms for the hyperspectral CD problem. Specifically, we use synthetic hyperspectral images with different spatial resolutions acquired at different altitudes, thus exhibiting different atmospheric affects. The importance of this work is in definition of a CD taxonomy and using that taxonomy for the accurate evaluation of several CD methods. Results are presented using receiver operating characteristic (ROC) curves and the area under the ROC curve, indicating that, under mildly varying imaging conditions, principal component analysis-based CD outperforms simple image differencing and correlation coefficient-based CD methods.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"91 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":"124832614","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: 8