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

Virtual dimensionality estimation for hyperspectral imagery with a fractal-based method 基于分形的高光谱图像虚拟维数估计方法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594955

Q. Du

引用次数: 9

Modeling the hyperspectral reflectance signal of moist sandy soils 湿润沙质土壤高光谱反射信号的模拟

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594865

B. Somers, W. Verstraeten, S. Delalieux, P. Coppin

引用次数: 0

SNNS application for crop classification using HyMap data 基于HyMap数据的SNNS作物分类应用

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594848

Dawid Olesiuk, M. Bachmann, M. Habermeyer, W. Heldens, Bogdan Zagajewski

引用次数: 2

Quantitative detection of sediment dust analog over green canopy using airborne hyperspectral imagery 利用机载高光谱图像定量检测绿冠层沉积物尘埃模拟物

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594842

A. Brook, E. Ben-Dor

{"title":"Quantitative detection of sediment dust analog over green canopy using airborne hyperspectral imagery","authors":"A. Brook, E. Ben-Dor","doi":"10.1109/WHISPERS.2010.5594842","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594842","url":null,"abstract":"A smart unmixing approach for quantitative detection of small amounts of dust that settle on the vegetation canopy using hyperspectral (HRS) airborne imagery data is proposed. A dust analog composed of Alumina (Aluminum Oxide Al2O3) powder was artificially spread over vegetation that covered 4 × 4 pixels of the AISA-Dual sensor. The alumina spectral signal could not be extracted using ordinary methods such as supervised classification (e.g. SAM or MTMF), unsupervised classification (Maximum Likelihood or Minimum Distance), and linear unmixing (e.g. MESMA or VCA). Considering the limitations of the above methods for extracting endmembers in a nonlinear domain, we developed a new approach that is capable of detecting the alumina powder from HRS imagery covering the VIS-NIR-SWIR (400–2400 nm) spectral regions. This step wised approach is based on a sequence merge between a decision tree algorithm, several spectral indices and a flexible constrained nonlinear unmixing method. The endmember vectors and abundances are obtained through a gradient-based optimization approach. Ground-truth examination of the results showed that the method is reliable and that it may represent a new frontier for assessing sediment dust contamination on a dark background via airborne sensors.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"44 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":"130402477","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

Mapping sealed surfaces from CHRIS/Proba data: A multiple endmember unmixing approach 从CHRIS/Proba数据映射密封表面:多端元解混方法

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594905

L. Demarchi, F. Canters, J. Chan, T. Voorde

引用次数: 3

Classification of high-mountain plant communities using artificial neural nets and hyperspectral data 基于人工神经网络和高光谱数据的高山植物群落分类

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594849

Bogdan Zagajewski

引用次数: 2

Spatial-spectral preprocessing for volume-based endmember extraction algorithms using unsupervised clustering 基于体的端元提取算法的无监督聚类空间光谱预处理

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594886

G. Martín, A. Plaza

{"title":"Spatial-spectral preprocessing for volume-based endmember extraction algorithms using unsupervised clustering","authors":"G. Martín, A. Plaza","doi":"10.1109/WHISPERS.2010.5594886","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594886","url":null,"abstract":"Spectral unmixing is an important task in hyperspectral data exploitation. This approach first identifies a collection of spectrally pure constituent spectra, called endmembers, and then expresses the measured spectrum of each mixed pixel as a combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember present in the pixel. Over the last decade, several algorithms have been developed for automatic extraction of spectral end-members using volume-based concepts. These algorithms use the spectral information contained in the data, and often neglect the spatial information. In this paper, we develop a novel spatial-spectral preprocessing technique for volume-based endmember extraction algorithms intended to exploit spectral information more effectively by adequately incorporating spatial context. Our experimental results, conducted using a real hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite Mining district in Nevada, reveal that the proposed approach can successfully integrate the spatial and spectral information contained in the input hyperspectral data.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"114 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":"126033447","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

Passive infrared hyperspectral imaging for standoff detection of tetryl explosive residue on a steel surface 被动红外高光谱成像技术在钢表面四乙基炸药残留检测中的应用

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594839

N. Gallagher, J. Kelly, T. Blake

引用次数: 5

Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument 基于稀疏性的有色噪声高光谱天体物理数据去噪:在MUSE仪器上的应用

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594902

S. Bourguignon, D. Mary, É. Slezak

{"title":"Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument","authors":"S. Bourguignon, D. Mary, É. Slezak","doi":"10.1109/WHISPERS.2010.5594902","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594902","url":null,"abstract":"This paper proposes a denoising method for hyperspectral astro-physical data, adapted to the specificities of the MUSE (Multi-Unit Spectroscopic Explorer) instrument, which will provide massive integral field spectroscopic observations of the far universe, characterized by very low signal-to-noise ratio and strongly non identically distributed noise. Data are considered as a collection of spectra. The proposed restoration procedure operates on each spectrum by minimizing a penalized data-fit criterion, which takes into account the noise spectral distribution, with additional constraints expressing prior sparsity information in a union of bases. Spectra are modeled as the sum of line and continuous spectra, which are supposed to be sparse in the canonical and the Discrete Cosine Transform bases, respectively. Dealing with colored noise requires specific methodological approaches regarding not only the estimator definition itself, but also hyperparameter tuning and optimization issues. These three points are successively investigated. Promising denoising results are obtained on realistic simulations of astrophysical observations.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"18 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":"116567497","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: 16

Robust hyperspectral data unmixing with spatial and spectral regularized NMF 基于空间正则化NMF和光谱正则化NMF的鲁棒高光谱数据解混

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594915

A. Huck, M. Guillaume

引用次数: 22