2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第9页

Target discrimination via optimal wavelength band selection with synthetic hyperspectral imagery 基于合成高光谱图像的最佳波段选择目标识别

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594873

Zhong Lu, Andrew Rice, J. Vasquez, J. Kerekes

引用次数: 3

Statistical analysis of growth levels of rice paddy based on hyperspectral imagery with high spatial resolution 基于高空间分辨率高光谱影像的水稻生长水平统计分析

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594954

K. Uto, Y. Kosugi, Jiro Sasaki

{"title":"Statistical analysis of growth levels of rice paddy based on hyperspectral imagery with high spatial resolution","authors":"K. Uto, Y. Kosugi, Jiro Sasaki","doi":"10.1109/WHISPERS.2010.5594954","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594954","url":null,"abstract":"Hyperspectral image data with sub-centimeter spatial resolution acquired by a low-altitude imaging system provided us valuable insight for the biochemistry. However, it is rather difficult to utilize the spatially detailed information because of the spectral fluctuation caused by the structural factor, e.g. BRDF, specular components, shading. This paper provides a statistical method for the estimation of growth levels in rice paddy based on hyperspectral image data with spatially high resolution. The extraction of vegetation regions under direct sun is followed by gaussian mixture modeling to separate different parts in the vegetation regions, e.g. leaves and ears in rice paddy. BRDF characteristics of specular components are utilized for simple specular component removal from the vegetation regions. The extracted spectral data are mapped to a feature space spanned by scaling factor-tolerant vegetation indices. Principal component analysis (PCA) with order constraint is used to generate indices which quantify growth levels of 5 paddy fields with different planting dates.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"65 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":"133478953","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: 2

Thermal infrared radiosity and heat diffusion model verification and validation 热红外辐射和热扩散模型的验证和验证

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594932

I. Danilina, A. Gillespie, Matthew Smith, L. Balick, E. Abbott

引用次数: 1

Automated Neutral Region selection using superpixels 使用超像素自动选择中性区域

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594856

L. Mandrake, D. Thompson, M. Gilmore, R. Castaño, E. Dobrea

引用次数: 2

A physically based approach in retrieving vegetation Leaf Area Index from Landsat surface reflectance data 基于物理的Landsat地表反射率数据反演植被叶面积指数方法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594875

S. Ganguly, R. Nemani, Y. Knyazikhin, Weile Wang, H. Hashimoto, P. Votava, A. Michaelis, C. Milesi, J. Dungan, F. Melton, R. Myneni

引用次数: 5

Spectral Angle Mapper classification of fluorescence hyperspectral image for aflatoxin contaminated corn 黄曲霉毒素污染玉米荧光高光谱图像的光谱角映射器分类

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594920

H. Yao, Zuzana Hruska, R. Kincaid, Ambrose E. Ononye, Robert L. Brown, T. Cleveland

{"title":"Spectral Angle Mapper classification of fluorescence hyperspectral image for aflatoxin contaminated corn","authors":"H. Yao, Zuzana Hruska, R. Kincaid, Ambrose E. Ononye, Robert L. Brown, T. Cleveland","doi":"10.1109/WHISPERS.2010.5594920","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594920","url":null,"abstract":"Aflatoxin contamination in corn is a serious problem for both producers and consumers. The present study applied the Spectral Angle Mapper classification technique to classify single corn kernels into contaminated and healthy groups. Fluorescence hyperspectral images were used in the classification. Actual corn aflatoxin concentration was chemically determined using the VICAM analytical method for quantification purpose. An obvious fluorescence peak shift was observed to be associated with the aflatoxin contaminated corn. Aflatoxin classification levels were based on Food and Drug Administration's regulation, including 20 ppb (parts per billion) for human consumption and 100 ppb for feed. Classification accuracy for the 20 ppb level is 86% with a false positive rate of 15%. For the 100 ppb level, the accuracy is 88% with a false positive rate of 16%. The results indicate that the Spectral Angle Mapper method and fluorescence hyperspectral imagery have the potential to classify aflatoxin contaminated corn kernels.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"148 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":"116341144","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: 15

Iterative spatial filtering for reducing intra-class spectral variability and noise 迭代空间滤波降低类内频谱变异性和噪声

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594871

Derek M. Rogge, B. Rivard

引用次数: 11

Detection of misallocated endmembers through the network based method 基于网络的端元错配检测方法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594857

Sykas Dimitris, Karathanassi Vassilia

引用次数: 1

Hyperspectral remote sensing of vegetation growing condition and regional environment 植被生长状况与区域环境的高光谱遥感研究

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594859

Bing Zhang

{"title":"Hyperspectral remote sensing of vegetation growing condition and regional environment","authors":"Bing Zhang","doi":"10.1109/WHISPERS.2010.5594859","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594859","url":null,"abstract":"A growing nμmber of studies in recent years have focused on how to use remote sensing for dynamic monitoring and effective evaluation of vegetation conditions and vegetation growing environment in mining areas, which will provide a scientific basis for making policies for controlling the environment in mining areas. In this paper, airborne hypersectral remote sensing data — HyMap images in the Mount Lyell mining area of Australia and Hyperion images in Dexing copper mining area of China were used. Analyses based on the biogeochemical effect of vegetation and living creatures in the mining area and the vegetation spectrμm and vegetation indices, two vegetation indices: Vegetation Inferiority Index (VII) and Water Absorption Decorrelative Index (WDI) have been used and developed. Experimental results show that VII can effectively reveal the vegetation growth conditions and growing environment in the mining area. The sensitivity of VII is shown to be superior to the traditional vegetation index — NDVI. This has also been verified by the application of Hyperion image-derived VII in Dexing copper mining area. WDI can effectively identify the area that contains hematite, especially the hematite areas that are covered with sparse vegetation. The two proposed indices are effective indicators for ecological environmental monitoring in mining areas.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"49 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":"127410780","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

Detection of occluded targets using thermal imaging spectroscopy 利用热成像光谱检测被遮挡目标

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594934

M. Shimoni, C. Perneel, J. Gagnon

引用次数: 5