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

Dirichlet process based context learning for mine detection in hyperspectral imagery 基于Dirichlet过程的高光谱图像中地雷探测的上下文学习

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594926

K. Morton, P. Torrione, L. Collins

{"title":"Dirichlet process based context learning for mine detection in hyperspectral imagery","authors":"K. Morton, P. Torrione, L. Collins","doi":"10.1109/WHISPERS.2010.5594926","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594926","url":null,"abstract":"Hyperspectral imagery (HSI) has been shown to be a powerful remote sensing phenomenology that is appropriate for a variety of classification and detection tasks. Standard detection and classification algorithms applied to hyperspectral data are hindered by environmental factors that alter the statistics of the data such as sun intensity, atmospheric conditions or soil properties. Detection and classification algorithms operating on HSI must account for the changing context underlying each observation for robust performance. This work focuses on algorithms that incorporate knowledge of underlying context for the discrimination of landmine responses from other surface or sub-surface anomalies using airborne HSI. This work compares both generative context models, that model context at a given location using features of the surrounding data, and discriminative context models that determine the context at a given location to maximize performance. Both approaches utilize a Dirichlet process prior to infer the number of contexts within the data without the need to explicitly label the context of each image or location within the image. Results indicate that Dirichlet process based generative context clustering determines contexts that are congruent with physical characteristics such as time of day, but does not necessarily lead to performance improvements. Dirichlet process based discriminative clustering, however, yields performance greater than a labeled generative approach.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"63 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":"116893924","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

Monte Carlo based hyperspectral scene simulation 基于蒙特卡罗的高光谱场景模拟

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594835

R. Sundberg, S. Richtsmeier, R. Haren

引用次数: 5

Effects of signature mismatch on hyperspectral detection algorithms 特征不匹配对高光谱检测算法的影响

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594824

D. Manolakis, T. Cooley, J. Jacobson

引用次数: 3

Spatial multiple instance learning for hyperspectral image analysis 用于高光谱图像分析的空间多实例学习

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594916

Jeremy Bolton, P. Gader

引用次数: 3

A physical interpretation of the correlation between canopy albedo and nitrogen using hyperspectral data 利用高光谱数据对冠层反照率与氮相关性的物理解释

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594889

M. Schull, Liang Xu, Y. Knyazikhin, R. Myneni

{"title":"A physical interpretation of the correlation between canopy albedo and nitrogen using hyperspectral data","authors":"M. Schull, Liang Xu, Y. Knyazikhin, R. Myneni","doi":"10.1109/WHISPERS.2010.5594889","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594889","url":null,"abstract":"Recent studies have shown that there is a high correlation between canopy nitrogen and NIR reflectance and subsequently canopy albedo. We provide a physical explanation for the correlation using the spectral invariants of the radiative transfer. The spectral invariant approach allows for a very accurate parameterization of the canopy reflectance in terms of the wavelength dependant single scattering albedo and two spectrally invariant and structurally varying parameters-recollision and escape probabilities. The spectral invariant parameters depend on macro-scale structural features such as crown shape and size, the proportion of sunlit and shaded leaves and ground cover, as well as micro-scale information such as within crown foliage distribution. We retrieve the spectral invariant parameters from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data for 3 sites in New England and 2 sites in the southeastern United States for which ground data on mass-based foliar %N were available. Theoretical and statistical analyses showed that canopy structure is highly correlated to canopy albedo, R2=94, suggesting that canopy structure is a dominant factor causing observed variation in NIR albedo. We therefore hypothesize that the amount of canopy nitrogen may have an indirect impact on NIR albedo through the formation of macro-scale features. Finally we show that we can predict the amount of canopy nitrogen more accurately using the macro-scale features than canopy albedo indicating that competing factors at the leaf and canopy scales are imbued in the measured albedo signal.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"131 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":"126012409","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

Reflectance spectra of RAMI stands in Estonia RAMI的反射光谱位于爱沙尼亚

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594845

A. Kuusk, Joel Kuusk, Mait Lang, T. Nilson

引用次数: 1

Hyperspectral target detection in a whitened space utilizing forward modeling concepts 利用正演建模概念的白化空间高光谱目标检测

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594939

Emmett Ientilucci, P. Bajorski

引用次数: 5

A new algorithm for local background suppression in hyperspectral target detection 高光谱目标检测中一种新的局部背景抑制算法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594906

S. Matteoli, N. Acito, M. Diani, G. Corsini

引用次数: 1

Interest segmentation of hyperspectral imagery 高光谱图像的兴趣分割

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594834

A. Schlamm, D. Messinger, William F Basener

引用次数: 3

Adapting learning parameter transition in the Generalized Learning Vector Quantization family of classifiers 广义学习向量量化分类器族中的自适应学习参数转移

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594950

S. Bischoff, M. Mendenhall, Andrew Rice, J. Vasquez

引用次数: 4