IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003最新文献_第6页

Hydrometeor classification system using dual-polarization radar measurements 水流星分类系统采用双极化雷达测量

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295197

Sanghun Lim, V. Chandrasekar

引用次数: 9

Hyperspectral analysis, the support vector machine, and land and benthic habitats 高光谱分析，支持向量机，以及陆地和底栖生物栖息地

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295215

J. A. Gualtieri

{"title":"Hyperspectral analysis, the support vector machine, and land and benthic habitats","authors":"J. A. Gualtieri","doi":"10.1109/WARSD.2003.1295215","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295215","url":null,"abstract":"Two different areas of current research in hyperspectral remote sensing are addressed: (1) supervised learning using all the hyperspectral bands as based on the recently introduced method called the support vector machine. (2) Hyperspectral remote sensing in shallow water to retrieve benthic properties including depth and albedo on the sea floor. The support vector technique is applied to land agricultural scenes acquired by AVIRIS with up to 16 classes, and is shown to give improved results over a number of methods all applied to the same scene. Hyperspectral remote sensing in shallow water is demonstrated on an AVIRIS scene acquired in Kaneohe Bay Hawaii, where reasonable depths and bottom albedos are retrieved. The method is based on physical modeling of the propagation of light though the atmosphere and physical modeling of the propagation of light through the water column above the sea floor. The results for shallow water remote sensing are extended by a physically realistic simulation using AVIRIS at-sensor data to model cases of spatial resolution and signal to noise ratios that might exist for a hyperspectral sensor in geostationary orbit.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121144299","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

Some recent results on hyperspectral image classification 高光谱图像分类的最新研究成果

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295214

C. A. Shah, P. Watanachaturaporn, P. Varshney, Manoj K. Arora

引用次数: 59

Compression of hyperspectral images with enhanced discriminant features 具有增强判别特征的高光谱图像压缩

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295176

Chulhee Lee, E. Choi

引用次数: 9

Comparison between constrained energy minimization based approaches for hyperspectral imagery 基于约束能量最小化的高光谱成像方法比较

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295199

H. Ren, Q. Du, Chein-I. Chang, J. Jensen

引用次数: 36

Towards a statistical error estimate for convex-hull derived endmembers 凸壳端元的统计误差估计

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295184

W.W. Stoner

{"title":"Towards a statistical error estimate for convex-hull derived endmembers","authors":"W.W. Stoner","doi":"10.1109/WARSD.2003.1295184","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295184","url":null,"abstract":"The convex hull methods for estimating spectral endmembers are subject to bias errors: mixed pixel bias - if all of the available pixels are mosaics of all m endmembers, the convex-hull derived endmember spectra are biased towards the centroid of the true endmember spectra; noise bias - additive Gaussian measurement noise inflates the convex hull away from the centroid of the noise-free convex hull. The noise bias error grows with the pixel count. This vulnerability to mixed pixel bias and noise bias prompts the following questions. Does the convex hull method throw away information by discarding the pixels lying inside the convex hull? Can bias error estimates be developed for convex-hull derived endmembers? Can bias-resistant endmember estimation methods be found? What is the gain in accuracy of the endmember estimates with increasing pixel count? What is the gain in accuracy with increasing density of pixels in the n-dimensional neighborhood of the true endmember? The following analysis focuses on these questions by omitting all sources of noise and distortion except the number and distribution of the samples in the neighborhood of the endmember.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130812386","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

De-noising remotely sensed digital imagery 遥感数字图像去噪

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295193

S. Chettri, W. Campbell

引用次数: 4

A residual-based approach to classification of remote sensing images 基于残差的遥感图像分类方法

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295224

L. Bruzzone, L. Carlin, F. Melgani

引用次数: 4

Wavelet-based watermarking of remotely sensed imagery tailored to classification performance 适合分类性能的遥感图像小波水印

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295202

S.B. Ziegeler, H. Tamhankar, J. Fowler, L. Bruce

引用次数: 17

Improving the accuracy of linear pixel unmixing via appropriate endmember dimensionality reduction 通过适当的端元降维来提高线性像素解混的精度

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295187

Jiang Li, L. Bruce

{"title":"Improving the accuracy of linear pixel unmixing via appropriate endmember dimensionality reduction","authors":"Jiang Li, L. Bruce","doi":"10.1109/WARSD.2003.1295187","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295187","url":null,"abstract":"Spectral unmixing is a quantitative analysis procedure used to recognize constituent ground cover materials (or endmembers) and obtain their mixing proportions (or abundances) from a mixed pixel. The endmember abundances may be estimated using the least squares estimation (LSE) method based on the linear mixture model. This paper investigates the use of spectral dimensionality reduction as a preprocessing tool for hyperspectral linear unmixing. Four dimensionality reduction methods are investigated and compared; these include methods based on the discrete wavelet transform (DWT), discrete cosine transform, principal component transform, and linear discriminant transform (LDT). Three sets of experiments are designed and implemented for evaluating the effects of the dimensionality reduction techniques on the LSE of endmember abundances. Experimental results show that the use of the DWT and LDT-based features extracted from the original hyperspectral signals can greatly improve the abundance estimation of endmembers. On average with these methods, the root-mean-square of the abundance estimation error is reduced by 20%.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122294630","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: 10