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2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献

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Autonomous lightweight airborne spectrometers for ground reflectance measurements 用于地面反射率测量的自主轻型机载光谱仪
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594827
Joel Kuusk, A. Kuusk
{"title":"Autonomous lightweight airborne spectrometers for ground reflectance measurements","authors":"Joel Kuusk, A. Kuusk","doi":"10.1109/WHISPERS.2010.5594827","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594827","url":null,"abstract":"A series of small, lightweight, autonomous spectrometer systems has been designed in Tartu Observatory. They are used for obtaining top-of-canopy spectral reflectance measurements for vegetation remote sensing, validation of radiative transfer models, and reflectance-based vicarious calibration of satellite and airborne sensors. The spectrometer systems are based on miniature spectrometer modules by Carl Zeiss Jena GmbH. The radiometric properties of the modules have been characterized based on laboratory measurements and appropriate data correction methods have been developed. The UAVSpec series spectrometer systems have been in operational use for several years and have been proven to be valuable tools for airborne, as well as on-ground and laboratory measurements. They are suitable for being carried by an unmanned aerial vehicle, which could make the measurement truly autonomous and independent of any service provider.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130721529","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: 7
Simplifying Support Vector Machines for classification of hyperspectral imagery and selection of relevant features 简化支持向量机用于高光谱图像的分类和相关特征的选择
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594937
Andreas Rabe, S. Linden, P. Hostert
{"title":"Simplifying Support Vector Machines for classification of hyperspectral imagery and selection of relevant features","authors":"Andreas Rabe, S. Linden, P. Hostert","doi":"10.1109/WHISPERS.2010.5594937","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594937","url":null,"abstract":"Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number of support vectors, and b) reducing the number of features by selecting relevant, non-redundant features. Results for three classification problems are shown. By applying the two approaches, we observe reduction rates a) between 9.1% and 27.2% for the number of support vectors and b) from 86.8% to 93.0% of features, both without significant decreases in classification accuracy. This enables a fast mapping of complete hyperspectral scenes.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131359131","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: 9
Hyperspectral tecniques for water quality monitoring: Application to the “Sacca di Goro” — Italy 高光谱技术用于水质监测:在意大利“Sacca di Goro”的应用
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594927
L. Alberotanza, Federica Braga, R. Cavalli, S. Pignatti, F. Santini
{"title":"Hyperspectral tecniques for water quality monitoring: Application to the “Sacca di Goro” — Italy","authors":"L. Alberotanza, Federica Braga, R. Cavalli, S. Pignatti, F. Santini","doi":"10.1109/WHISPERS.2010.5594927","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594927","url":null,"abstract":"The paper presents a comparison between an empirical algorithm and a physics based model for the assessment of water compound concentrations by remote sensing hyperspectral data. At the purpose a series of in situ measurements were carried out monthly, from June to October 2005, to spectrally characterize the water of the “Sacca di Goro” (Italy) at spatial (horizontal and vertical) and temporal (daily and seasonal) scales. The results obtained by the application of the two different methods to the in situ acquired data showed that an appreciable improvement is obtainable by considering the physical approach.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342041","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
Current airborne LWIR HSI remote sensing activities at Defence R&D Canada — Valcartier 目前机载LWIR HSI遥感活动在加拿大国防研发-瓦尔卡地亚
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594880
E. Puckrin, C. Turcotte, J. Lévesque, J. Thériault, H. Lavoie, François Bouffard
{"title":"Current airborne LWIR HSI remote sensing activities at Defence R&D Canada — Valcartier","authors":"E. Puckrin, C. Turcotte, J. Lévesque, J. Thériault, H. Lavoie, François Bouffard","doi":"10.1109/WHISPERS.2010.5594880","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594880","url":null,"abstract":"Recently, DRDC Valcartier has been investigating longwave hyperspectral imaging (HSI) remote sensing techniques using airborne sensors. There is currently an initiative to test the commercially available ground-based Hyper-Cam HSI system, developed by Telops, on a stabilized airborne platform with an integrated image motion compensation capability. The Hyper-Cam is also based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320×256 pixels at spectral resolutions of up to 0.25 cm−1. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. Airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in this paper.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114509214","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
Study of the influence of pre-processing on local statistics-based anomaly detector results 预处理对局部统计异常检测结果的影响研究
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594922
D. Borghys, C. Perneel
{"title":"Study of the influence of pre-processing on local statistics-based anomaly detector results","authors":"D. Borghys, C. Perneel","doi":"10.1109/WHISPERS.2010.5594922","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594922","url":null,"abstract":"Anomaly detection in hyperspectral data has received much attention for various applications and is especially important for defense and security applications. Anomaly detection detects pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra [1]. Most existing methods estimate the spectra of the (local or global) background and then detect anomalies as pixels with a large spectral distance w.r.t. the determined background spectra. Many types of anomaly detectors have been proposed in literature. This paper reports on a sensitivity study that tries to determine an adequate pre-processing chain for anomaly detection in hyperspectral scenes. The study is performed on a set of five hyperspectral datasets and focuses on statistics-based anomaly detectors.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114931402","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
Image simulation of geostationary sensor dedicated to ocean color 海洋色彩专用地球同步传感器的图像仿真
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594969
Manchun Lei, A. Minghelli-Roman, S. Mathieu, P. Gouton
{"title":"Image simulation of geostationary sensor dedicated to ocean color","authors":"Manchun Lei, A. Minghelli-Roman, S. Mathieu, P. Gouton","doi":"10.1109/WHISPERS.2010.5594969","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594969","url":null,"abstract":"A method of image simulation of geostationary sensor dedicated to ocean color for open water (case1) and coastal water (case2) is presented in this paper. This method uses HYDROLIGHT to model the radiative transfer in order to obtain the water surface radiance. MeRIS level 3 products have been used for input water components to provide a realistic spatial distribution. The atmospheric radiative transfer model and the sensor model finely lead to satellite remote sensing images. This system allows to evaluate the dynamic range of BOA and TOA radiances depending on solar and viewing angles in operational situation and latter their influence on water composition retrieval.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132299255","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
Impact of collinearity on linear and nonlinear spectral mixture analysis 共线性对线性和非线性光谱混合分析的影响
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594918
Xuehong Chen, Jin Chen, X. Jia, Jin Wu
{"title":"Impact of collinearity on linear and nonlinear spectral mixture analysis","authors":"Xuehong Chen, Jin Chen, X. Jia, Jin Wu","doi":"10.1109/WHISPERS.2010.5594918","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594918","url":null,"abstract":"Linear and nonlinear spectral mixture analysis has been studied for deriving the fractions of spectrally pure materials in a mixed pixel in the past decades. However, not much attention has been given to the collinearity problem in spectral unmixing. In this paper, quantitative analysis and detailed simulations are provided which show that the high correlation between the endmembers, including the virtual endmembers introduced in a nonlinear model, has a strong impact on unmixing errors through inflating the Gaussian noise. While distinctive spectra with low correlations are often selected as true endmembers, the virtual endmembers formed by their product terms can be highly correlated with others. Therefore, it is found that a nonlinear model generally suffers the collinearity problem more in comparison with a linear model and may not perform as expected when the Gaussian noise is high, despite its higher modeling power. Experiments were conducted to illustrate the effects.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132438432","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
Calibration of a chemometrical model from field hyperspectral close-range images: Taking into account leaf inclination and multiple reflection effects 从野外高光谱近距离图像校正化学计量模型:考虑叶片倾斜和多重反射效应
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594904
N. Vigneau, G. Rabatel, P. Roumet, M. Ecarnot
{"title":"Calibration of a chemometrical model from field hyperspectral close-range images: Taking into account leaf inclination and multiple reflection effects","authors":"N. Vigneau, G. Rabatel, P. Roumet, M. Ecarnot","doi":"10.1109/WHISPERS.2010.5594904","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594904","url":null,"abstract":"The aim of the study was to test the potentiality of field close-range hyperspectral images to obtain nitrogen content of wheat leaves in field. Field measurements imply several potential disturbances of the recorded signal: illumination level, specular reflection, multiple reflections, etc. Chemo-metrical model calibration procedure has to take them into account. We tested the following methodology. First we calibrated a chemometrical model between nitrogen content and non-disturbed reflectance signal of flat isolated leaves. Thus we evaluated the quality of such a model. Then, we identified and qualified each phenomenon effects and proposed a solution to overcome each issue. Finally, we verified that all the disturbances could be taken into account while preserving satisfactory model prediction quality.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125946080","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
Using spatial correspondences for hyperspectral knowledge transfer: Evaluation on synthetic data 利用空间对应进行高光谱知识转移:对合成数据的评价
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594944
B. Bue, E. Merényi
{"title":"Using spatial correspondences for hyperspectral knowledge transfer: Evaluation on synthetic data","authors":"B. Bue, E. Merényi","doi":"10.1109/WHISPERS.2010.5594944","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594944","url":null,"abstract":"We describe a proof of concept for class knowledge transfer from a labeled hyperspectral image to an unlabeled image, captured with a different (hyper-/multi-spectral) sensor, when the spatial extents of the images partially overlap. By defining a set of spatio-spectral correspondences between the labeled source image and the unlabeled target image, we create a mapping between the images we can use to propagate labels from the source to the target image. This mapping allows us to classify the target image using the source labels without manually defining training labels in the target image. We evaluate the technique using state of the art synthetic hyperspectral imagery.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130334557","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
Spectral-textural endmember extraction 光谱结构端元提取
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594909
M. Zortea, D. Tuia, F. Pacifici, A. Plaza
{"title":"Spectral-textural endmember extraction","authors":"M. Zortea, D. Tuia, F. Pacifici, A. Plaza","doi":"10.1109/WHISPERS.2010.5594909","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594909","url":null,"abstract":"Several available techniques for endmember extraction and spectral unmixing use only the spectral information contained in the hyperspectral data. In this paper, we introduce a novel method for spatial-spectral endmember extraction which incorporates texture features in the quantification of spatial information (jointly with spectral information). Experimental results with simulated and real hyperspectraldata sets indicate that textural information could assist the extraction of spectral endmembers, although a challenging issue still remains: how to combine the final set of endmember candidates (obtained by merging the individual sets of candidates found using spectral, textural and joint spectral-textural information) in order to provide a relevant final solution.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127303846","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
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