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

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Using airborne hyperspectral data to characterize the surface pH of pyrite mine tailings 利用航空高光谱数据对黄铁矿尾矿表面pH值进行表征
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289015
Natalie Zabcic, B. Rivard, C. Ong, A. Müller
{"title":"Using airborne hyperspectral data to characterize the surface pH of pyrite mine tailings","authors":"Natalie Zabcic, B. Rivard, C. Ong, A. Müller","doi":"10.1109/WHISPERS.2009.5289015","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289015","url":null,"abstract":"High spatial-resolution Hymap airborne hyperspectral data was used to generate predictive pH maps of acid mine drainage (AMD) for the Sotiel-Migollas mine complex, Southwest Spain. These maps portray the spatial distribution of highly acidic areas, which are likely associated with high concentrations of heavy metals. A predictive pH model was built using partial least squares (PLS) analysis to determine the relationship between the spectral response of AMD samples and their leachate pH measured in the laboratory. A validation of the model for an independent data set shows a r2 of 0.71 between actual and predicted pH values. Hyperspectral imagery is shown to provide an effective means to quantitatively pinpoint sources of acidity.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"9 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":"127429519","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: 14
Melanosome level estimation in human skin from hyperspectral imagery 基于高光谱图像的人体皮肤黑素体水平估计
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289039
Abel S. Nunez, M. Mendenhall, K. Gross
{"title":"Melanosome level estimation in human skin from hyperspectral imagery","authors":"Abel S. Nunez, M. Mendenhall, K. Gross","doi":"10.1109/WHISPERS.2009.5289039","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289039","url":null,"abstract":"Locating individuals in the open has several practical uses; most formidable is that of the search and rescue application. Although existing methods exist to find human skin in color imagery, these methods are subject to high false alarm rates caused by objects that are skin colored. Hyperspectral imagery offers a distinct advantage due to the abundance of spectral information that can be exploited to dramatically reduce false alarms while maintaining a high detection rate. The work presented in this article extends our earlier work in hyperspectral-based skin detection to the detection of skin pigmentation levels. Specifically, we estimate the amount of melanosomes contained within pixels identified as skin which gives an estimate of skin color. Our method is based on the intrinsic properties of human skin and does not use a “hyperspectral to RGB conversion.” We demonstrate the capability of our algorithm using a hyperspectral instrument developed by SpecTIR Corp (the HST3) which nominally covers the spectral range of 400–2500nm.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"26 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":"122222316","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
The cost of time - implications of hyperspectral data volume and feature selection routines for conservation science 高光谱数据量对时间成本的影响及特征选择方法在守恒科学中的应用
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289056
M. Kalacska, J. Arroyo-Mora
{"title":"The cost of time - implications of hyperspectral data volume and feature selection routines for conservation science","authors":"M. Kalacska, J. Arroyo-Mora","doi":"10.1109/WHISPERS.2009.5289056","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289056","url":null,"abstract":"The recent greater availability of airborne hyperspectral imagery in the tropics has allowed for the analysis of increasingly complex analytical questions in ecology such as remote tree species identification. In comparison to species identification the spectral expression of gender in dioecious species has been generally overlooked despite its effects on plant ecophysiological functioning and the prevalence of dioecious species in the tropics. A problem often implied but not frequently addressed in these analyses is the complexity posed by the data volume collected by airborne sensors. We examine the effect of this volume specifically on feature selection routines for classification and the implication of the resultant limitations on the use of airborne hyperspectral imagery at regional operational scales. We conclude based on an examination of analytical time and the cost of high performance computing systems, that an efficient alternative for such large scale academic or NGO research is a cluster of PlayStation™ 3s.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"15 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":"132477519","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: 0
Sensitivity of hyperspectral classification algorithms to training sample size 高光谱分类算法对训练样本大小的敏感性
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288983
Matthew A. Lee, S. Prasad, L. Bruce, Terrance R. West, Daniel Reynolds, T. Irby, H. Kalluri
{"title":"Sensitivity of hyperspectral classification algorithms to training sample size","authors":"Matthew A. Lee, S. Prasad, L. Bruce, Terrance R. West, Daniel Reynolds, T. Irby, H. Kalluri","doi":"10.1109/WHISPERS.2009.5288983","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288983","url":null,"abstract":"Algorithms that exploit hyperspectral imagery often encounter problems related to the high dimensionality of the data, particularly when the amount of training data is limited. Recently, two algorithms were proposed to alleviate the small sample size problem - one is based on employing a Multi-Classifier Decision Fusion (MCDF) in the raw reflectance domain, and the other employed the MCDF framework in the Discrete Wavelet Transform domain (DWT-MCDF). This paper investigates the sensitivity of conventional single classifier based classification approaches, as well as MCDF and DWT-MCDF to variations in the amount of data employed for training the classification system. The hyperspectral data in this experiment was obtained using an airborne hyperspectral imager used by SpecTIR™. The results of the experimental analysis show that for the given application, the MCDF and DWT-MCDF algorithms are significantly less sensitive than the conventional algorithms to limited training data. PCA consistently results in overall accuracies of about 35%. LDA accuracies are very high, about 75%, when there is an abundance of training data - about 10X (i.e. number of training samples is 10 times the number of spectral bands); remains above 60% for training data abundances of 2X and higher; but dramatically decreases to ∼20% for abundances of 1X. MCDF results in accuracies ranging between 65% and 75% for training data abundance of 3X and higher, but the accuracies drop to ∼60% for 2X and ∼55% for 1X. DWT-MCDF results in high accuracies with the least sensitivity to training data abundance. Its accuracies range between ∼60–65% for abundances of 1X to 10X.","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":"132732385","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: 18
Remote spectral detection using a laboratory signature 使用实验室签名的远程光谱检测
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289061
A. Schaum
{"title":"Remote spectral detection using a laboratory signature","authors":"A. Schaum","doi":"10.1109/WHISPERS.2009.5289061","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289061","url":null,"abstract":"Two new algorithms are derived for remotely detecting a material characterized only by its laboratory spectrum. The methods are motivated by the practical difficulties in predicting an accurate field radiance from a reflectance. The first algorithm associates an affine subspace with the material, instead of a radiance point. The second algorithm is designed to prevent false alarms from dark pixels, to which the first algorithm may be sensitive. Both algorithms are ideally suited for use in conjunction with a simple method of vicarious calibration, which is also described.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"105 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131727390","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
Deconvolution of VNIR spectra using modified Gaussian modeling (MGM) with automatic parameter initialization (API) applied to CRISM 应用于CRISM的改进高斯模型(MGM)和自动参数初始化(API)对近红外光谱进行反卷积
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289046
H. D. Makarewicz, M. Parente, J. Bishop
{"title":"Deconvolution of VNIR spectra using modified Gaussian modeling (MGM) with automatic parameter initialization (API) applied to CRISM","authors":"H. D. Makarewicz, M. Parente, J. Bishop","doi":"10.1109/WHISPERS.2009.5289046","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289046","url":null,"abstract":"Reflectance spectroscopy is a powerful tool for determining mineralogy in both laboratory and field experiments. Several studies indicate that reflectance spectra can be modeled as a sum of modified Gaussian functions and a continuum, which is called the modified Gaussian model (MGM). In this study, a method for automatic parameter initialization (API) for the MGM is proposed that is based solely on the spectrum being modeled. The API determines the number of Gaussians to model and their initial parameter estimates. The MGM with API has been tested with artificial, laboratory, and CRISM spectra. Initial results indicate that the method is successful on a variety of mineral spectra.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"71 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":"123852271","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: 13
Fast forward modeling of Titan's infrared spectra to invert VIMS/Cassini hyperspectral images 快进建模的土卫六红外光谱反演VIMS/卡西尼号高光谱图像
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289065
S. Rodriguez, S. Mouélic, P. Rannou, J. Combe, L. Corre, G. Tobie, J. Barnes, C. Sotin, Robert H. Brown, K. Baines, B. Buratti, R. Clark, P. Nicholson
{"title":"Fast forward modeling of Titan's infrared spectra to invert VIMS/Cassini hyperspectral images","authors":"S. Rodriguez, S. Mouélic, P. Rannou, J. Combe, L. Corre, G. Tobie, J. Barnes, C. Sotin, Robert H. Brown, K. Baines, B. Buratti, R. Clark, P. Nicholson","doi":"10.1109/WHISPERS.2009.5289065","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289065","url":null,"abstract":"The surface of Titan, the largest icy moon of Saturn, is veiled by a very thick and hazy atmosphere. The Visual and Infrared Mapping Spectrometer onboard the Cassini spacecraft, in orbit around Saturn since July 2004, conduct an intensive survey of Titan with the objective to understand the complex nature of the atmosphere and surface of the mysterious moon and the way they interact. Accurate radiative transfer modeling is necessary to analyze Titan’s infrared spectra, but are often very computer resources demanding. As Cassini has gathered hitherto millions of spectra of Titan and will still observe it until at least 2010, we report here on the development of a new rapid, simple and versatile radiative transfer model specially designed to invert VIMS datacubes.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"26 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":"116020341","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
Calibration pipeline of VIRTIS-M onboard Venus Express 金星快车上VIRTIS-M的校准管道
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289087
A. C. Moinelo, G. Piccioni, E. Ammannito
{"title":"Calibration pipeline of VIRTIS-M onboard Venus Express","authors":"A. C. Moinelo, G. Piccioni, E. Ammannito","doi":"10.1109/WHISPERS.2009.5289087","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289087","url":null,"abstract":"The Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS) is flying on board the ESA mission Venus Express and orbiting around the planet Venus since April 11 2006, providing very valuable remote sensing data of the planet. The instrument combines a double capability: high-resolution visible and infrared imaging in the 0.28–5 µm range at moderate spectral resolution (VIRTIS-M channel) and high-resolution spectroscopy in the 2–5 µm range (VIRTIS-H channel). The scientific objectives of VIRTIS cover a large field and span from the study of the surface up to the upper atmosphere. The team is composed by people coming from institutes abroad from more than 10 countries. About 2.5 Gbit of raw compressed data are coming in average every day from the spacecraft to be further processed and distributed to the team for the data analysis. Here we described how the pipeline is structured and the various different steps performed from the telemetry to the calibrated data products but focused on VIRTIS-M. We also present some example of data product.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"17 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":"114516389","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: 0
Optimal band selection for future satellite sensor dedicated to soil science 未来土壤科学卫星传感器的最佳波段选择
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289053
S. Kandasamy, A. Minghelli-Roman, François Tavin, S. Mathieu, F. Baret, P. Gouton
{"title":"Optimal band selection for future satellite sensor dedicated to soil science","authors":"S. Kandasamy, A. Minghelli-Roman, François Tavin, S. Mathieu, F. Baret, P. Gouton","doi":"10.1109/WHISPERS.2009.5289053","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289053","url":null,"abstract":"Hyperspectral imaging systems could be used for identifying the different soil types from the satellites. However, detecting the reflectance of the soils in all the wavelengths involves the use of a large number of sensors with high accuracy and also creates a problem in transmitting the data to earth stations for processing. The current sensors can reach a bandwidth of 20 nm and hence, the reflectance obtained using the sensors are the integration of reflectance obtained in each of the wavelength present in the spectral band. Moreover, not all spectral bands contribute equally to classification and hence, identifying the bands necessary to have a good classification is necessary to reduce sensor cost and problem in data transmission from the satellite. The work presents the spectral bands selected using a PCA-Based Forward Sequential band selection algorithm.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"18 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":"114192318","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: 3
Applying linear spectral unmixing to airborne hyperspectral imagery for mapping crop yield variability 将线性光谱分解应用于航空高光谱图像，用于绘制作物产量变异性
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289022
Chenghai Yang, J. Everitt, J. Bradford
{"title":"Applying linear spectral unmixing to airborne hyperspectral imagery for mapping crop yield variability","authors":"Chenghai Yang, J. Everitt, J. Bradford","doi":"10.1109/WHISPERS.2009.5289022","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289022","url":null,"abstract":"This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from a grain sorghum field and a cotton field. A pair of plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover fractions. Yield was positively related to plant fractions and negatively related to soil fractions. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Plant fractions provided better correlations with yield than the majority of the NDVIs. These results indicate that plant cover fraction maps derived from hyperspectral imagery can be used as relative yield maps to characterize crop yield variability.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"32 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":"114376637","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: 0
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