发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Feature selection based on Ant Colony algorithm for hyperspectral remote sensing images 基于蚁群算法的高光谱遥感图像特征选择
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594966
F. Samadzadegan, T. Partovi
{"title":"Feature selection based on Ant Colony algorithm for hyperspectral remote sensing images","authors":"F. Samadzadegan, T. Partovi","doi":"10.1109/WHISPERS.2010.5594966","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594966","url":null,"abstract":"Nowadays, hyper-spectral remote sensing imaging systems are able to acquire several hundreds of spectral bands. Increasing spectral bands provide the more information for land cover and separate similarity classes and classification accuracy potentially could increase. Nevertheless classification of hyper-spectral imagery by conventional classifiers suffers from Hughes phenomenon. Namely, by increasing spectral bands, for a fixed number of training samples, classification accuracy is reduced. One of the solutions for overcoming the mentioned problem is reducing the dimension of input space based on feature selection techniques. Traditional feature selection techniques have several limitations in performance and finding the global optimum subset selection of feature in hyper-spectral images. In this paper a novel feature selection algorithms based on an Ant Colony Optimization (ACO) presents. ACO techniques are based on the behavior of real ant colonies. Evaluating of obtained results from classification accuracy of AVIRIS image data set shows effectiveness of this algorithm as it achieves fewer features and higher classification accuracy rather than other non-parametric optimization methods such as Genetic Algorithm.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"41 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":"121850387","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: 17
Modeling recollision and escape probabilities using the stochastic radiative transfer equation 利用随机辐射传递方程模拟回忆和逃逸概率
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594885
Liang Xu, M. Schull, R. Myneni, Y. Knyazikhin
{"title":"Modeling recollision and escape probabilities using the stochastic radiative transfer equation","authors":"Liang Xu, M. Schull, R. Myneni, Y. Knyazikhin","doi":"10.1109/WHISPERS.2010.5594885","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594885","url":null,"abstract":"The spectral invariants of vegetation canopy convey a lot of information on the canopy structure at hierarchical levels. Recent findings of the wavelength independent and scale independent variable — the ratio between the directional escape and the total escape probability — show that it does dependent on the selection of reference leaf albedo in getting correct reflectance values and can be treated as the identifier of macro scale canopy structure (foliage density, aspect ratio, ground cover, tree shape). In order to better utilize this variable in the retrieval algorithm for 3D canopy structure. Model simulation based on the stochastic radiative transfer equation is used to test the sensitivity of this variable to the structural parameters. Hyperspectral and multi-angle data are simulated and analyzed.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"3 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":"132683234","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
On the modeling of hyperspectral imaging data with elliptically contoured distributions 椭圆轮廓分布的高光谱成像数据建模研究
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594836
S. Niu, V. Ingle, D. Manolakis, T. Cooley
{"title":"On the modeling of hyperspectral imaging data with elliptically contoured distributions","authors":"S. Niu, V. Ingle, D. Manolakis, T. Cooley","doi":"10.1109/WHISPERS.2010.5594836","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594836","url":null,"abstract":"Accurate statistical models for hyperspectral imaging (HSI) data are fundamental for many subsequent applications including detection, classification, and estimation. Suppose the whole nonhomogeneous HSI data is well classified into homogeneous unimodal clutters, we find that the family of elliptically contoured distributions (ECDs) is capable of providing sufficiently accurate model for each clutter. In this paper, several techniques are applied to test the elliptical symmetry of HSI clutters. Instead of testing elliptical symmetry directly, its counterpart spherical symmetry is examined for the whitened unimodal clutters. For each clutter which passes these symmetry checking tests, fitting an appropriate ECD based model to the data can be done in the Mahalanobis distance direction.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"1 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":"133982180","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: 3
Non-negative Matrix Factorization under sparsity constraints to unmix in vivo spectrally resolved acquisitions 在稀疏性约束下的非负矩阵分解来解调体内光谱分辨图像
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594850
Anne-Sophie Montcuquet, L. Hervé, F. Navarro, J. Dinten, J. Mars
{"title":"Non-negative Matrix Factorization under sparsity constraints to unmix in vivo spectrally resolved acquisitions","authors":"Anne-Sophie Montcuquet, L. Hervé, F. Navarro, J. Dinten, J. Mars","doi":"10.1109/WHISPERS.2010.5594850","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594850","url":null,"abstract":"Fluorescence imaging in diffusive media is an emerging imaging modality for medical applications which uses injected fluorescent markers (several ones may be simultaneously injected) that bind to specific targets, as tumors. The region of interest is illuminated with near infrared light and the emitted back fluorescence is analyzed to localize the fluorescence sources. To investigate thick medium, as the fluorescence signal decreases with the light travel distance, any disturbing signal, such as biological tissues intrinsic fluorescence — called autofluorescence —, is a limiting factor. To remove autofluorescence and isolate each specific fluorescent signal from the others, a spectroscopic approach, based on Non-negative Matrix Factorization, is explored. We ran an NMF algorithm with sparsity constraints on experimental data, and successfully obtained separated in vivo fluorescence spectra.","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":"134119288","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: 1
The angular kernel in machine learning for hyperspectral data classification 高光谱数据分类机器学习中的角核
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594908
P. Honeine, C. Richard
{"title":"The angular kernel in machine learning for hyperspectral data classification","authors":"P. Honeine, C. Richard","doi":"10.1109/WHISPERS.2010.5594908","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594908","url":null,"abstract":"Support vector machines have been investigated with success for hyperspectral data classification. In this paper, we propose a new kernel to measure spectral similarity, called the angular kernel. We provide some of its properties, such as its invariance to illumination energy, as well as connection to previous work. Furthermore, we show that the performance of a classifier associated to the angular kernel is comparable to the Gaussian kernel, in the sense of universality. We derive a class of kernels based on the angular kernel, and study the performance on an urban classification task.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"53 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":"124231492","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: 20
Controlled spectral unmixing using extended Support Vector Machines 利用扩展支持向量机控制光谱分解
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594843
X. Jia, C. Dey, D. Fraser, L. Lymburner, A. Lewis
{"title":"Controlled spectral unmixing using extended Support Vector Machines","authors":"X. Jia, C. Dey, D. Fraser, L. Lymburner, A. Lewis","doi":"10.1109/WHISPERS.2010.5594843","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594843","url":null,"abstract":"This paper presents an improved spectral unmixing framework for remote sensing data interpretation. Instead of unmixing every pixel in an image into a fixed set of endmembers, approaches of adaptive subsets of endmember selection for individual pixels are presented which can improve the performance of spectral unmixing. An integrated hard and soft classification map is then generated by applying the mixture analysis based on extended Support Vector Machines. The proposed treatment is effective and easy to implement. Unmixing is more reliable with the controlled mixture model. It can cope with the endmembers' spectral variation as a result of system noise encountered during data collection from the space. Experiments were conducted with Landsat ETM data and satisfactory results were achieved.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"82 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":"115870801","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: 19
Potential of continuum removed reflectance spectral features estimating nitrogen nutrition in rice canopy level 连续统势去除反射光谱特征估算水稻冠层氮营养
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594837
Jinheng Zhang
{"title":"Potential of continuum removed reflectance spectral features estimating nitrogen nutrition in rice canopy level","authors":"Jinheng Zhang","doi":"10.1109/WHISPERS.2010.5594837","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594837","url":null,"abstract":"A method was developed for estimating the nitrogen nutrition of rice (Oryza sativa) using canopy continuum-removed reflectance. The canopy leaves were sampled during important growth stages (tiller stage, jointing stage, booting stage and heading stage), and nitrogen concentrations and canopy reflectance were measured. The value of the continuum-removed reflectance (550nm–750nm) decreased with the nitrogen levels increased. Several characteristics of the continuum-removed reflectance spectra of rice canopy were calculated, including the continuum-removed reflectance value, the minimum of the continuum-removed absorption features, symmetry, total area of absorption peak and left area of the absorption peak. High correlations were found between the spectral characteristics of the continuum-removed reflectance and canopy leaves nitrogen concentrations. Linear regressions models were used to predicte nitrogen concentration. Results of this research showed that total area of absorption peak was the best predictor of nitrogen levels throughout growth cycle.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"109 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":"124829781","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
Using Random Matrix Theory to determine the number of endmembers in a hyperspectral image 利用随机矩阵理论确定高光谱图像中端元的数目
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594854
K. Cawse‐Nicholson, M. Sears, A. Robin, S. Damelin, K. Wessels, F. V. D. Bergh, R. Mathieu
{"title":"Using Random Matrix Theory to determine the number of endmembers in a hyperspectral image","authors":"K. Cawse‐Nicholson, M. Sears, A. Robin, S. Damelin, K. Wessels, F. V. D. Bergh, R. Mathieu","doi":"10.1109/WHISPERS.2010.5594854","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594854","url":null,"abstract":"Determining the number of spectral endmembers in a hyper-spectral image is an important step in the spectral unmixing process, and under- or overestimation of this number may lead to incorrect unmixing for unsupervised methods. In this paper we discuss a new method for determining the number of endmembers, using recent advances in Random Matrix Theory. This method is entirely unsupervised and is computationally cheaper than other existing methods. We apply our method to synthetic images, including a standard test image developed by Chein-I Chang, with good results for Gaussian independent noise.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"52 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":"131533649","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
Comparing the use of hyperspectral Irradiance Reflectance and Diffuse Attenuation Coeficient as indicators for algal presence in the water column 比较使用高光谱辐照反射率和漫射衰减系数作为水柱中藻类存在的指标
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594911
I. F. Aymerich, S. Pons, J. Piera, E. Torrecilla, Oliver N. Ross
{"title":"Comparing the use of hyperspectral Irradiance Reflectance and Diffuse Attenuation Coeficient as indicators for algal presence in the water column","authors":"I. F. Aymerich, S. Pons, J. Piera, E. Torrecilla, Oliver N. Ross","doi":"10.1109/WHISPERS.2010.5594911","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594911","url":null,"abstract":"Remarkable advances are being achieved on spectroradiometric systems technology, allowing the development of spectrometers aimed at in-situ measurements. However, the use of numerical modelling of the ocean optical properties can help to understand how these properties react to changes in the water column's composition. In this work, HydroLight-EcoLight was used in order to simulate different scenarios to study how two Apparent Optical Properties (AOPs) such as Irradiance Reflectance (R) and Diffuse Attenuation Coefficient (Kd) could be used to detect the presence of a specific phytoplankton group in the water column.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"1 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":"129089321","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: 1
A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model 基于非高斯混合模型的高光谱图像光谱异常检测器
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594901
Tiziana Veracini, S. Matteoli, M. Diani, G. Corsini, Sergio Ugo de Ceglie
{"title":"A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model","authors":"Tiziana Veracini, S. Matteoli, M. Diani, G. Corsini, Sergio Ugo de Ceglie","doi":"10.1109/WHISPERS.2010.5594901","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594901","url":null,"abstract":"Anomaly Detection (AD) in remotely sensed airborne hyperspectral images has been proven valuable in many applications. Within the AD approach that defines the spectral anomalies with respect to a statistical model for the background, reliable background PDF estimation is essential to a successful outcome. This paper proposes a new Bayesian strategy for learning a non-Gaussian mixture model for the background PDF based on elliptically contoured distributions. The resulting estimated background PDF is then used to detect spectral anomalies, characterized by a low probability of occurrence with respect to the global background, through the Generalized Likelihood Ratio Test (GLRT). Real hyperspectral imagery is used for experimental evaluation of the proposed strategy.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"101 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120986041","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信