2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第4页

Canopy vertical structure using MODIS Bidirectional Reflectance data 利用MODIS双向反射率数据研究冠层垂直结构

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594952

Zhuosen Wang, C. Schaaf, Philip Lewis, Y. Knyazikhin, M. Schull, A. Strahler, R. Myneni, M. Chopping

{"title":"Canopy vertical structure using MODIS Bidirectional Reflectance data","authors":"Zhuosen Wang, C. Schaaf, Philip Lewis, Y. Knyazikhin, M. Schull, A. Strahler, R. Myneni, M. Chopping","doi":"10.1109/WHISPERS.2010.5594952","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594952","url":null,"abstract":"Canopy spectral invariant variables, escape probability and recollision probability, are wavelength independent and intrinsic canopy structure properties. They provide a physical interpretation of the correlation between canopy architecture and multi-angle spectral data. The 500m Moderate resolution Imaging Spectroadiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product from study sites at Howland Forest, Maine are used to develop multivariate linear regression models to estimate canopy vertical structure using both escape probabilities and directional reflectance. These are compared with canopy height information which has been retrieved from the airborne Laser Vegetation Imaging Sensor (LVIS) at a finer scale spatial resolution. Both the escape probability and the directional reflectance approaches achieve similar results with correlation coefficients of 0.63–0.66. This suggests that the MODIS 500m BRDF data can be useful in extrapolating limited lidar information on canopy vertical structure to larger regional areas.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"37 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":"115633557","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

Multiple model endmember detection based on spectral and spatial information 基于光谱和空间信息的多模型端元检测

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594866

Ouiem Bchir, H. Frigui, Alina Zare, P. Gader

引用次数: 7

GTI method for wilt oak trees detection 枯萎栎树GTI检测方法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594858

Massimo Dell'Erba, K. Uto

引用次数: 0

Classification of hyperspectral images by using morphological attribute filters and Independent Component Analysis 基于形态属性滤波和独立分量分析的高光谱图像分类

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594964

M. Mura, A. Villa, J. Benediktsson, J. Chanussot, L. Bruzzone

引用次数: 5

Independent Component Discriminant Analysis for hyperspectral image classification 高光谱图像分类的独立分量判别分析

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594853

Alberto Villa, J. Benediktsson, Jocelyn Chanussot, Christian Jutten

引用次数: 13

A modified Pixel Purity Index method for hyperspectral images 一种改进的高光谱图像像素纯度指数方法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594948

P. Bajorski, N. J. Sanders

引用次数: 3

Spectral image complexity estimated through local convex hull volume 利用局部凸包体积估计光谱图像复杂度

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594869

D. Messinger, A. Ziemann, A. Schlamm, Bill Basener

{"title":"Spectral image complexity estimated through local convex hull volume","authors":"D. Messinger, A. Ziemann, A. Schlamm, Bill Basener","doi":"10.1109/WHISPERS.2010.5594869","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594869","url":null,"abstract":"Most spectral image processing schemes develop models of the data in the hyperspace by using first and second order statistics or linear subspace geometries applied to the image globally. However, it is simple to show that the data are typically not multivariate Gaussian or are not well defined by linear geometries when considering the entire image, particularly as the spatial resolution improves and the scene becomes more cluttered. Here, we use the concept of a convex hull that encloses the data to rank local regions within an image by an estimate of their complexity. The complexity as defined here is directly related to the volume of the hull in n dimensions that encloses the data under the assumptions that less complex data will have fewer distinct materials and more complex data will have more materials. They will also be more widely separated in the hyperspace. The method uses the Gram Matrix approach to estimate the volume of the hull and is applied to an image that has been tiled. The complexity of each tile is then estimated showing the relative changes in complexity over a large area spectral image. Results will be shown for reflective hyperspectral imagery over different scene contents with resolutions of ≈2–3 m. Ultimately this methodology can be used to develop localized models of an image and may provide insight into the large area search problem.","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":"130974834","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: 27

Radiometric characterisation of a VNIR hyperspectral imaging system for accurate atmospheric correction 用于精确大气校正的近红外高光谱成像系统的辐射特性

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594838

L. Martínez, F. Pérez, R. Arbiol, A. Tarda

{"title":"Radiometric characterisation of a VNIR hyperspectral imaging system for accurate atmospheric correction","authors":"L. Martínez, F. Pérez, R. Arbiol, A. Tarda","doi":"10.1109/WHISPERS.2010.5594838","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594838","url":null,"abstract":"The Institut Cartogàfic de Catalunya (ICC) regularly operates a Compact Airborne Spectral Imager (CASI) sensor. For this system an atmospheric correction algorithm was developed to simultaneously correct multiple overlapping images taken from different heights. First, the algorithm estimates the main atmospheric parameters with an inversion procedure using either radiometric ground measurements or image homologous areas plus a single ground measurement. Then, the code is applied to the images to obtain atmospherically corrected hyperspectral imagery. The algorithm was applied in the frame of ICC-Banyoles 2005 experiment (Spain) using multi-height imagery and field simultaneous reflectance measurements. In the validation step, the standard deviations obtained with both inversion methods were similar. In order to improve these results, the smiling effect (spectral shift) for the sensor is characterized by locating O2 absorption bands in the NIR for each CASI look direction. Additionally, a more accurate spectral sensitivity for each band has been calculated. These improvements are applied to EuroSDR-Banyoles 2008 experiment's (Spain) imagery. These results show a substantial improvement on the atmospheric correction at the absorption regions when compared to field reflectance measurements. This behaviour advises the inclusion of these developments in the inversion system.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"42 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":"129910451","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

Estimating canopy spectral invariants from ground reference and remote sensing data 从地面参考资料和遥感数据估计冠层光谱不变量

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594861

M. Rautiainen, P. Stenberg, P. Lukeš, M. Mõttus, J. Heiskanen

引用次数: 2

Accuracy and performance of optimized Bayesian Source Separation for hyperspectral unmixing 优化贝叶斯源分离用于高光谱解混的精度和性能

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594851

F. Schmidt, A. Schmidt, E. Tréguier, Maël Guiheneuf, S. Moussaoui, N. Dobigeon

{"title":"Accuracy and performance of optimized Bayesian Source Separation for hyperspectral unmixing","authors":"F. Schmidt, A. Schmidt, E. Tréguier, Maël Guiheneuf, S. Moussaoui, N. Dobigeon","doi":"10.1109/WHISPERS.2010.5594851","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594851","url":null,"abstract":"Bayesian Source Separation (BPSS) with non-negativity constraints is a useful unsupervised approach for hyperspectral data unmixing. The main goal of this approach is to ensure the non-negativity of the unmixed source spectra as well as of the abundances. Moreover, a recent extension has been proposed to impose the sum-to-one (full additivity) constraint on the estimated source abundances of each pixel. Unfortunately, even though non-negativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms is limited by high computation time and large memory requirements since these Bayesian algorithms employ Markov Chain Monte Carlo methods. This article describes an implementation strategy which allow to apply such algorithms on a full hyperspectral image, of typical size in Earth and Planetary Sciences, with reasonable computational cost. In this paper, not only optimizations on the technical level are proposed but we also study the effect of convex hull pixel selection as a preprocessing and sampling step and discuss the impact of such preprocessing on the relevance of the estimated component spectra and abundance maps, as well as on the whole computation times. For that purpose, two different datasets are employed: a synthetic one and a real hyperspectral image from Mars.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"3 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":"127281997","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