2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第10页

Unsupervised endmember extraction of martian hyperspectral images 火星高光谱图像的无监督端元提取

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289070

B. Luo, J. Chanussot, S. Douté, X. Ceamanos

引用次数: 3

Wheat straw composition and spectral reflectance changes during decomposition 麦秸成分和光谱反射率在分解过程中的变化

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289086

C. Daughtry, G. Serbin, J. Reeves, P. Doraiswamy, E. Hunt

引用次数: 1

Spectral band discrimination for species observed from hyperspectral remote sensing 高光谱遥感观测物种的光谱波段判别

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289067

N. Dudeni, P. Debba, M. Cho, R. Mathieu

{"title":"Spectral band discrimination for species observed from hyperspectral remote sensing","authors":"N. Dudeni, P. Debba, M. Cho, R. Mathieu","doi":"10.1109/WHISPERS.2009.5289067","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289067","url":null,"abstract":"In vegetation spectroscopy, compositional information of leaves contained at band level or across the electromagnetic spectrum (EMS) and parts thereof, plays a huge rule in the analysis of spectra and their relations to the reflectance patterns across the spectrum. Spectral matching is often achieved by means of matching algorithms such as the Spectral Angle Mapper (SAM), Spectral information divergence (SID) and mixed measures of SAM and SID using either the tangent or the sine trigonometric functions, SID(TAN) or SID(SIN). The performance of these measures in distinguishing between objects of interest, such as species, is often compared using the relative spectral discriminatory probability (RSDPB). In this study, these measures are used to assess whether various sets of bands including the full spectrum, the visible (VIS), the near infrared (NIR), the shortwave infra-red (SWIR) region, as well as sets of bands identified by the stepwise discriminant analysis (SDA), can be used to discriminate the different species. This is done to identify the important regions of the EMS to distinguish seven common savannah tree species observed in the Kruger National Park, South Africa's largest game reserve. The magnitude of variation of the species in any part of the spectrum can be linked to the importance of that spectral region in distinguishing the species. In addition, classification accuracy of these sets of bands was assessed and the SDA bands often gave better classification accuracy compared to using all bands, bands in the NIR, and SWIR parts of the EMS.","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":"122219028","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

Forward Modeling and Atmospheric Compensation in hyperspectral data: Experimental analysis from a target detection perspective 高光谱数据的正演模拟与大气补偿:从目标探测角度的实验分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288972

S. Matteoli, Emmett Ientilucci, J. Kerekes

{"title":"Forward Modeling and Atmospheric Compensation in hyperspectral data: Experimental analysis from a target detection perspective","authors":"S. Matteoli, Emmett Ientilucci, J. Kerekes","doi":"10.1109/WHISPERS.2009.5288972","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288972","url":null,"abstract":"Taking into account atmospheric effects is crucial in target detection of airborne/satellite hyperspectral images. In regard to this, two physics-based approaches to atmospheric radiative transfer modeling are considered here: Atmospheric Compensation (AC) and Forward Modeling (FM). An experimental analysis is presented that encompasses target detection both relying upon an atmospherically compensated reflectance image and by generating predicted radiance target spaces through a forward modeling approach. Real hyperspectral imagery that embodies a very challenging, cluttered, mixed pixel detection problem is used to compare AC and FM approaches from an operational target detection perspective. On this data, detection in the radiance domain through FM has proven to be as effective as the standard AC plus reflectance domain processing. Experiments have also highlighted several aspects of FM approach (e.g. its intrinsic simplicity, flexibility, and applicability) that should be considered when performing target detection, especially for targets affected by high variability.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"23 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":"133792407","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

An NCM-based Bayesian algorithm for hyperspectral unmixing 基于ncm的高光谱解混贝叶斯算法

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289102

O. Eches, N. Dobigeon, J. Tourneret

引用次数: 7

Hyperspectral video for illumination-invariant tracking 用于光照不变跟踪的高光谱视频

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289103

A. Banerjee, P. Burlina, Joshua B. Broadwater

引用次数: 34

The comparison of pixel size and atmospheric correction method on matched filter detection for a hyperspectral image 高光谱图像匹配滤波检测中像元大小与大气校正方法的比较

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289000

P. Conforti, R. Sundberg

引用次数: 1

A comparison of kernel functions for intimate mixture models 密切混合模型核函数的比较

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289073

Joshua B. Broadwater, A. Banerjee

引用次数: 61

Impact of different morphological profiles on the classification accuracy of urban hyperspectral data 不同形态轮廓对城市高光谱数据分类精度的影响

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289078

B. Waske, S. Linden, J. Benediktsson, Andreas Rabe, P. Hostert

{"title":"Impact of different morphological profiles on the classification accuracy of urban hyperspectral data","authors":"B. Waske, S. Linden, J. Benediktsson, Andreas Rabe, P. Hostert","doi":"10.1109/WHISPERS.2009.5289078","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289078","url":null,"abstract":"We present a detailed study on the classification of urban hyperspectral data with morphological profiles (MP). Although such a spectral-spatial classification approach may significantly increase achieved accuracy, the computational complexity as well as the increased dimensionality and redundancy of such data sets are potential drawbacks. This can be overcome by feature selection. Moreover it is useful to derive detailed information on the contribution of different components from MP to the classification accuracy by evaluating these subsets. We apply a wrapper approach for feature selection based on support vector machines (SVM) with sequential feature forward selection (FFS) search strategy to two hyperspectral data sets that contain the first principal components (PC) and various corresponding MP from an urban area. In doing so, we identify feature subsets of increasing size that perform best in terms of kappa for the given setup. Results clearly demonstrate that maximum classification accuracies are achieved already on small feature subsets with few morphological profiles.","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":"123939974","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

Local covariance matrices for improved target detection performance 局部协方差矩阵用于提高目标检测性能

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288987

C. E. Caefer, S. Rotman

引用次数: 4