2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第9页

Spectral unmixing via minimum volume simplices: Application to near infrared spectra of counterfeit tablets 通过最小体积简化法分离光谱:用于假冒片剂的近红外光谱

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289081

M. Lopes, J. Bioucas-Dias, Mário A. T. Figueiredo, J. Wolff

{"title":"Spectral unmixing via minimum volume simplices: Application to near infrared spectra of counterfeit tablets","authors":"M. Lopes, J. Bioucas-Dias, Mário A. T. Figueiredo, J. Wolff","doi":"10.1109/WHISPERS.2009.5289081","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289081","url":null,"abstract":"Counterfeit pharmaceutical products pose a serious public health problem. It is thus important not only to detect them, but also to identify their composition and assess the risk for the patient. Identifying the spectral signatures of the pure compounds present in a (maybe counterfeit) tablet of unknown origin is clearly a hyperspectral unmixing problem. In fact, under a linear mixing model, the hyperspectral vectors belong to a simplex whose vertices are the spectra of the pure compounds in the mixture. Minimum volume simplex analysis (MVSA) and minimum-volume enclosing simplex (MVES) are recently proposed algorithms, exploiting the idea of finding a simplex of minimum volume fitting the observed data. This work gives evidence of the usefulness of MVES and MVSA for unmixing near infrared (NIR) hyperspectral data of tablets of unknown composition. Experiments reported in this paper show that MVES and MVSA strongly outperform the state-of-the-art method in analytical chemistry for spectral unmixing: multivariate curve resolution - alternating least squares (MCR-ALS). These experiments are based on synthetic data (studying the effect of noise and of the presence/absence of pure pixels) and on a real dataset composed of NIR hyperspectral images of counterfeit tablets.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"34 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":"123779029","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: 6

In-situ networks on test-sites in support of space earth observations 支持空间地球观测的试验场现场网络

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289109

R. Kancheva, D. Borisova, G. Georgiev

{"title":"In-situ networks on test-sites in support of space earth observations","authors":"R. Kancheva, D. Borisova, G. Georgiev","doi":"10.1109/WHISPERS.2009.5289109","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289109","url":null,"abstract":"Acknowledged and justified is the recognition of remote sensing as a powerful tool in land cover/land use monitoring for a large number of purposes ranging from agricultural practices to global ecology and environment protection. Data collected and information created from Earth observations constitute critical inputs to the sustainable management of the Earth - providing evidence for informed decision-making. However, policies on the environment often suffer by having to rely on information that is fragmentary and of uneven quality and value despite of the considerable progress that has been made in space-borne observation systems and information technologies. Currently, the remote sensing community is recognizing again, in a more complex and systematical way, the indispensable necessity of ground-truth information in support of satellite Earth observation missions. In order the remote sensing techniques to be widely transferred to operative applications, data accuracy and information reliability is critical. Algorithms and quantitative models for estimating various land surface variables from remotely sensed observations need to be validated using geo-reference data. Supporting and raising the capacity of remote sensing investigations encompasses the implementation of a wide range of information sources, making full use of ground-based in-situ monitoring as well as of airborne surveys and space-based observations. In this context the paper presents a vision on the objectives, the infrastructure and the functioning of in-situ networks for data acquisition on target selected test-sites with the aim to enhance remote sensing scientific and modelling capacities and to meet the need for multidisciplinary research and multipurpose data application relying both on technology developments and data accuracy. The paper aims also at rising the interest in international networking and collaboration.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"116 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":"114547042","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: 1

Efficient implementation of morphological opening and closing by reconstruction on multi-core parallel systems 基于重构的多核并行系统形态学开闭的高效实现

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289002

D. Valencia, A. Plaza

引用次数: 10

An iterative least squares approach to decorrelate minerals and ices contributions in hyperspectral images: Application to Cuprite (earth) and Mars 用迭代最小二乘方法解相关高光谱图像中矿物和冰的贡献:在铜(地球)和火星上的应用

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289003

S. Mouélic, J. Combe, V. Sarago, N. Mangold, M. Masse, J. Bibring, B. Gondet, Y. Langevin, C. Sotin

{"title":"An iterative least squares approach to decorrelate minerals and ices contributions in hyperspectral images: Application to Cuprite (earth) and Mars","authors":"S. Mouélic, J. Combe, V. Sarago, N. Mangold, M. Masse, J. Bibring, B. Gondet, Y. Langevin, C. Sotin","doi":"10.1109/WHISPERS.2009.5289003","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289003","url":null,"abstract":"We present an Iterative Linear Spectral Unmixing Model (ILSUM) which is aimed at finding the main surface components that contribute to the signal in visible and infrared hyperspectral images. We processed the global dataset of the OMEGA imaging spectrometer onboard Mars Express up to orbit 5300, covering two martian years. We also present a preliminary test on AVIRIS data on the Cuprite (Nevada) site. We use ILSUM to identify the contribution of each endmember of an input library containing laboratory spectra of ices and mineral powders that are representative of the main mineral families. Synthetic spectra (pure slope endmembers) are included to account at first order for aerosol and grain size variations. Applied to the global OMEGA data set, this algorithm provides a distribution map for the main minerals present on the martian surface, which appears to be mainly dominated by pyroxenes, olivine, ferric oxides, with localized exposures of sulfates and phyllosilicates.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"323-325 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":"130877308","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: 10

Subpixel roughness effects in spectral thermal infrared emissivity images 光谱热红外发射率图像中的亚像素粗糙度效应

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288976

I. Danilina, A. Gillespie, L. Balick, A. Mushkin, Matthew Smith, M. O'Neal

引用次数: 5

Kernel-based Linear Spectral Mixture Analysis for hyperspectral image classification 基于核函数的线性光谱混合分析在高光谱图像分类中的应用

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289096

Keng-Hao Liu, E. Wong, Chein-I. Chang

引用次数: 11

Speeding up the MATLAB™ Hyperspectral Image Analysis Toolbox using GPUs and the Jacket Toolbox 使用gpu和夹克工具箱加速MATLAB™高光谱图像分析工具箱

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289089

S. Rosario-Torres, M. Velez-Reyes

{"title":"Speeding up the MATLAB™ Hyperspectral Image Analysis Toolbox using GPUs and the Jacket Toolbox","authors":"S. Rosario-Torres, M. Velez-Reyes","doi":"10.1109/WHISPERS.2009.5289089","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289089","url":null,"abstract":"The Hyperspectral Image Analysis Toolbox (HIAT) is a MATLAB™ toolbox for the analysis of hyperspectral imagery. HIAT includes a collection of algorithms for processing of hyperspectral and multispectral imagery under the MATLAB environment. The objective of HIAT is to provide a suite of information extraction algorithms to users of hyperspectral and multispectral imagery across different application domains. HIAT has been developed as part of the NSF Bernard M. Gordon Center for Subsurface Sensing and Imaging Solutionware that seeks to develop a repository of reliable and reusable software tools that can be shared by researchers across research domains. HIAT includes feature extraction and selection, supervised and unsupervised classification algorithms, unmixing, and visualization algorithms developed at the UPRM Laboratory for Applied Remote Sensing and Image Processing. A key limitation of the MATLAB environment is its difficulty in managing large images. Here we investigate the use of the recently released MATLAB Jacket Toolbox that allows implementation of MATLAB programs in GPUs. This paper presents a comparison of the CPU implementation with the GPU implementation of different routines of HIAT.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"162 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":"124535345","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: 19

Retrieving Mars surface reflectance from OMEGA/MEX imagery 从OMEGA/MEX图像中获取火星表面反射率

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289063

S. Douté

引用次数: 8

Evaluation of oak wilt index based on genetic programming 基于遗传规划的橡树枯萎指数评价

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289107

K. Uto, Y. Kosugi, Toshinari Ogata

引用次数: 6

Machine learning techniques for the inversion of planetary hyperspectral images 行星高光谱图像反演的机器学习技术

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289010

C. Bernard-Michel, S. Douté, M. Fauvel, L. Gardes, S. Girard

引用次数: 4