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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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Canopy spectral invariants for remote sensing of canopy structure 冠层结构遥感的冠层光谱不变量
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289105
Y. Knyazikhin, M. Schull, Liang Hu, R. Myneni, P. Carmona
{"title":"Canopy spectral invariants for remote sensing of canopy structure","authors":"Y. Knyazikhin, M. Schull, Liang Hu, R. Myneni, P. Carmona","doi":"10.1109/WHISPERS.2009.5289105","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289105","url":null,"abstract":"The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral reflectances become wavelength independent and determine two canopy structure specific variables - the recollision and escape probabilities. The recollision probability (probability that a photon scattered from a phytoelement will interact within the canopy again) is a measure of the multi-level hierarchical structure in a vegetated pixel and can be obtained from hyperspectral data. The escape probability (probability that a scattered photon will escape the vegetation in a given direction) is sensitive to canopy geometrical properties and can be derived from multi-angle spectral data. The escape and recollision probabilities have the potential to separate forest types based on crown shape and the number of hierarchical levels within the landscape. This paper introduces the concept and demonstrates how this approach can be used to monitor forest structural parameters with multi-angle and hyperspectral data.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"313 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":"123408702","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
Spectral smile correction in CRISM hyperspectral images CRISM高光谱图像的光谱微笑校正
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288992
X. Ceamanos, S. Douté
{"title":"Spectral smile correction in CRISM hyperspectral images","authors":"X. Ceamanos, S. Douté","doi":"10.1109/WHISPERS.2009.5288992","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288992","url":null,"abstract":"The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) is affected by an artifact common to pushbroom sensors: “spectral smile”. As a consequence, both central wavelength and spectral resolution become dependent on the across-track position thus giving rise to a horizontal spatial modulation. The correction of CRISM spectral smile is addressed using a two-step correction. First, data is re-sampled to the so-called “sweet spot” wavelengths. Secondly, the non-uniform spectral response width of the detection elements is overcome by mimicking an increase of resolution thanks to a spectral sharpening. Experiments show remarkable results regarding the decrease of smile energy.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"53 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":"125512827","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
Feature-aided tracking via synthetic hyperspectral imagery 通过合成高光谱图像进行特征辅助跟踪
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289035
Andrew Rice, J. Vasquez, M. Mendenhall, J. Kerekes
{"title":"Feature-aided tracking via synthetic hyperspectral imagery","authors":"Andrew Rice, J. Vasquez, M. Mendenhall, J. Kerekes","doi":"10.1109/WHISPERS.2009.5289035","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289035","url":null,"abstract":"Hyperspectral imaging (HSI) feature-aided tracking (FAT) is an emerging area of research, employing HSI instruments and exploitation techniques with the goal to track moving objects within challenging environments and across frequent ambiguities. A series of studies have been conducted to demonstrate HSI-FAT with contemporary and novel HSI instruments. Synthesized HSI data have been the key enabler to this effort. Capabilities have been evaluated with synthetic models of low-cost, off-the-shelf sensors such as a video-rate liquid crystal tunable filter, as well as sophisticated emerging sensor concepts such as microelectromechanical-adapted systems. A suite of end-to-end synthetic experiments have been conducted, which include high-fidelity moving-target urban vignettes, synthetic hyperspectral rendering, and full image-chain treatment of the various sensor models. Corresponding algorithm development has focused on motion segmentation, spectral feature modeling, classification, fused kinematic/spectral association, and adaptive sensor feedback/ control.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"241 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":"116146445","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
A novel adaptive classification method for hyperspectral data using manifold regularization kernel machines 基于流形正则化核机的高光谱数据自适应分类方法
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289052
Wonkook Kim, M. Crawford
{"title":"A novel adaptive classification method for hyperspectral data using manifold regularization kernel machines","authors":"Wonkook Kim, M. Crawford","doi":"10.1109/WHISPERS.2009.5289052","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289052","url":null,"abstract":"Remote sensing data sets are often difficult to compare directly due to environmental changes between acquisitions of two data sets. This paper proposes an adaptive framework for robust classification when no reference data are available in a new area or time period. Labels of test data are recovered during iterative applications of kernel machines by reflecting geometry of unlabeled samples via the manifold regularization term, so that the labeled/unlabeled samples form clusters on the data manifold. A one-against-one scheme is used for the extension of the binary classifier to multiclass problems, where semi-labels are used for iterative training of classifier. The proposed method is applied to a series of data pair of Hyperion and AVIRIS hyperspectral data and compared to other non-adaptive classification methods.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"84 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":"127019195","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: 12
Spatial resolution enhancement of Hyperion hyperspectral data Hyperion高光谱数据的空间分辨率增强
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288996
K. Nikolakopoulos
{"title":"Spatial resolution enhancement of Hyperion hyperspectral data","authors":"K. Nikolakopoulos","doi":"10.1109/WHISPERS.2009.5288996","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288996","url":null,"abstract":"In this study eight fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (Modihs), Pansharp and PCA, were used for the fusion of Hyperion hyperspectral data with ALI panchromatic data. Both sensors are part of the Earth-Observing 1 satellite. The panchromatic data have a spatial resolution of 10m while the hyperspectral data have a spatial resolution of 30m. All the fusion techniques are designed for use with classical multispectral data. Thus, it is quite interesting to investigate the assessment of the common used fusion algorithms with the hyperspectral data.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"49 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":"127043678","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: 4
Learning dependent sources using mixtures of Dirichlet: Applications on hyperspectral unmixing 使用狄利克雷混合学习依赖源:在高光谱解混中的应用
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288975
J. Nascimento, J. Bioucas-Dias
{"title":"Learning dependent sources using mixtures of Dirichlet: Applications on hyperspectral unmixing","authors":"J. Nascimento, J. Bioucas-Dias","doi":"10.1109/WHISPERS.2009.5288975","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288975","url":null,"abstract":"This paper is an elaboration of the DECA algorithm [1] to blindly unmix hyperspectral data. The underlying mixing model is linear, meaning that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. The proposed method, as DECA, is tailored to highly mixed mixtures in which the geometric based approaches fail to identify the simplex of minimum volume enclosing the observed spectral vectors. We resort then to a statitistical framework, where the abundance fractions are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. With respect to DECA, we introduce two improvements: 1) the number of Dirichlet modes are inferred based on the minimum description length (MDL) principle; 2) The generalized expectation maximization (GEM) algorithm we adopt to infer the model parameters is improved by using alternating minimization and augmented Lagrangian methods to compute the mixing matrix. The effectiveness of the proposed algorithm is illustrated with simulated and read data.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"7 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":"130355579","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: 17
Subspace selection based multiple classifier systems for hyperspectral image classification 基于子空间选择的多分类器高光谱图像分类系统
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288977
Bor-Chen Kuo, Chun-Hsiang Chuang, Cheng-Hsaun Li, Chin-Teng Lin
{"title":"Subspace selection based multiple classifier systems for hyperspectral image classification","authors":"Bor-Chen Kuo, Chun-Hsiang Chuang, Cheng-Hsaun Li, Chin-Teng Lin","doi":"10.1109/WHISPERS.2009.5288977","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288977","url":null,"abstract":"In a typical supervised classification task, the size of training data fundamentally affects the generality of a classifier. Given a finite and fixed size of training data, the classification result may be degraded as the number of features (dimensionality) increase. Many researches have demonstrated that multiple classifier systems (MCS) or so-called ensembles can alleviate small sample size and high dimensionality concern, and obtain more outstanding and robust results than single models. One of the effective approaches for generating an ensemble of diverse base classifiers is the use of different feature subsets such as random subspace method (RSM). The objective of this research is to develop a novel ensemble technique based on cluster algorithms for strengthening RSM. The results of real data experiments show that the proposed method obtains the sound performance especially in the situation of using less number of classifiers.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"68 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":"133742527","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
Simulation of the image generation process for CRISM spectrometer data CRISM光谱仪数据图像生成过程的仿真
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289001
M. Parente, J. Clark, A. Brown, J. Bishop
{"title":"Simulation of the image generation process for CRISM spectrometer data","authors":"M. Parente, J. Clark, A. Brown, J. Bishop","doi":"10.1109/WHISPERS.2009.5289001","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289001","url":null,"abstract":"Hyperspectral near-infrared scenes have been simulated to analyze the contributions of surface minerals, atmosphere and sensor noise on images of Mars. Modeling the remote sensing process creates a means for independent analysis of the influence of the environment and instruments on detection accuracy of the surface composition. The system models surface reflectance based on laboratory sample spectra, creates atmospheric effects using DISORT, simulates the instrument response function using CRISM data files and adds instrument noise from thermal and other sources. The purpose of this work is understanding the hyperspectral remote sensing process to eventually enable elevated detection accuracy of minerals on the surface of Mars.","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":"129691203","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
On the incorporation of spatial information to endmember extraction: Survey and algorithm comparison 空间信息在端元提取中的应用:综述与算法比较
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289024
A. Plaza, G. Martín, M. Zortea
{"title":"On the incorporation of spatial information to endmember extraction: Survey and algorithm comparison","authors":"A. Plaza, G. Martín, M. Zortea","doi":"10.1109/WHISPERS.2009.5289024","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289024","url":null,"abstract":"Several well-known algorithms have been used for endmember extraction and spectral unmixing of hyperspectral imagery by considering only the spectral properties of the data when conducting the search. However, it might be beneficial to incorporate the spatial arrangement of the data in the development of endmember extraction and spectral unmixing algorithms. In this paper, we provide a survey on the use of spatial information in endmember extraction and further compare six different algorithms (three of which only use spectral information) in order to substantiate the impact of using spatial-spectral information versus only spectral information when searching for image endmembers. The comparison is carried out using a synthetic hyperspectral scene with spatial patterns generated using fractals, and a real hyperspectral scene collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS).","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":"131219447","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
Onboard SVM analysis of Hyperion data to detect sulfur deposits in Arctic regions 北极地区硫矿床探测Hyperion数据的机载SVM分析
2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288999
L. Mandrake, K. Wagstaff, D. Gleeson, U. Rebbapragada, D. Tran, R. Castaño, Steve Ankuo Chien, R. Pappalardo
{"title":"Onboard SVM analysis of Hyperion data to detect sulfur deposits in Arctic regions","authors":"L. Mandrake, K. Wagstaff, D. Gleeson, U. Rebbapragada, D. Tran, R. Castaño, Steve Ankuo Chien, R. Pappalardo","doi":"10.1109/WHISPERS.2009.5288999","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288999","url":null,"abstract":"Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"6 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":"116198881","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: 4
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