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

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The application of spectral invariants for discrimination of crops using CHRIS-PROBA data 光谱不变量在CHRIS-PROBA数据作物识别中的应用
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594879
P. Carmona, M. Schull, Y. Knyazikhin, F. Pla
{"title":"The application of spectral invariants for discrimination of crops using CHRIS-PROBA data","authors":"P. Carmona, M. Schull, Y. Knyazikhin, F. Pla","doi":"10.1109/WHISPERS.2010.5594879","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594879","url":null,"abstract":"Numerous studies have demonstrated the ability of hyper-spectral data to discriminate crop types, however most methods rely on empirical data and are therefore site specific. In this brief proceeding we provide a physically based approach for separation of crop types using multiangle hyperspectral data. We use the radiative transfer theory of spectral invariants which allows for the parameterization of the canopy reflectance into two spectrally invariant and structurally varying parameters — recollision and escape probabilities. The spectral invariant parameters are retrieved from the CHRIS/PROBA multiangle hyperspectral sensor. We present the spectral invariant parameters in spectral invariant space. The horizontal axis provides information about macro scale features such as plant shape and size as well as ground cover. The vertical axis provides information about microscale features such as leaf density as well as portion of sunlit to shaded leaves. These features allow for the natural separation of crops. In addition we illustrate the potential for further separation of crop types based on angular information. Results suggest that multiangle information is important for canopies with similar structural features in the nadir direction.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"33 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":"127724893","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 evaluation of three endmember extraction algorithms: ATGP, ICA-EEA & VCA ATGP、ICA-EEA和VCA三种端元提取算法的评价
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594830
Isaac D. Gerg
{"title":"An evaluation of three endmember extraction algorithms: ATGP, ICA-EEA & VCA","authors":"Isaac D. Gerg","doi":"10.1109/WHISPERS.2010.5594830","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594830","url":null,"abstract":"In this paper, we evaluate three endmember extraction algorithms for use in hyperspectral imagery unmixing: automatic target generation procedure (ATGP), independent component analysis endmember extraction algorithm (ICA-EEA), and vertex component analysis (VCA). We evaluate each algorithm's ability to find known pure pixels in a scene of simulated data. Several variations of simulated data are used to thoroughly examine the unmixing limits of each algorithm.","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":"130042462","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: 14
Divergence based vector quantization of spectral data 基于散度的光谱数据矢量量化
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594946
T. Villmann, S. Haase
{"title":"Divergence based vector quantization of spectral data","authors":"T. Villmann, S. Haase","doi":"10.1109/WHISPERS.2010.5594946","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594946","url":null,"abstract":"Unsupervised and supervised vector quantization models for clustering and classification are usually designed for processing of Euclidean vectorial data. Yet, in this scenario the physical context might be not adequately reflected. For example, spectra can be seen as positive functions (positive measures). Yet, this context information is not used in Euclidean vector quantization. — In this contribution we propose a methodology for extending gradient based vector quantization approaches utilizing divergences as dissimilarity measure instead of the Euclidean distance for positive measures. Divergences are specifically designed to judge the dissimilarities between positive measures and have frequently an underlying physical meaning. We present in the paper the mathematical foundation for plugging divergences into vector quantization schemes and their adaptation rules. Thereafter, we demonstrate the ability of this methodology for the self-organizing map as widely ranged vector quantizer, applying it for topographic data clustering of a hyperspectral AVIRIS image cube taken from a lunar crater volcanic field.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"6 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":"126577082","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
Systematic detection of Titan's clouds in VIMS/Cassini hyperspectral images using a new automated algorithm 使用新的自动算法在VIMS/卡西尼号高光谱图像中系统地检测土卫六的云层
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594893
S. Rodriguez, F. Schmidt, S. Moussaoui, S. Mouélic, P. Rannou, J. Barnes, C. Sotin, Robert H. Brown, K. Baines, B. Buratti, R. Clark, P. Nicholson
{"title":"Systematic detection of Titan's clouds in VIMS/Cassini hyperspectral images using a new automated algorithm","authors":"S. Rodriguez, F. Schmidt, S. Moussaoui, S. Mouélic, P. Rannou, J. Barnes, C. Sotin, Robert H. Brown, K. Baines, B. Buratti, R. Clark, P. Nicholson","doi":"10.1109/WHISPERS.2010.5594893","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594893","url":null,"abstract":"Titan is the Saturn's largest moon where meteorological processes are very active, as observed most recently by the Cassini/Huygens orbiter. Cloud monitoring is a prime method to observe, describe and understand present climate on Titan. Unlike our previous detection method, which was based on manual control of threshold, we investigate here the possibility of a fully automated methodology based on blind source separation to analyzing years of Cassini near-infrared cloud images. Since the spectral signature of Titan clouds are diverse and not known a priori, the choice of a blind source separation seems to be appropriate. Preliminary results show that Titan's cloud detection is possible using the recent implementation of a Bayesian source separation method.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"8 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":"133133171","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
Anomaly detection in non-stationary backgrounds 非平稳背景中的异常检测
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594914
N. Gorelik, Hadar Yehudai, S. Rotman
{"title":"Anomaly detection in non-stationary backgrounds","authors":"N. Gorelik, Hadar Yehudai, S. Rotman","doi":"10.1109/WHISPERS.2010.5594914","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594914","url":null,"abstract":"In this paper, several algorithms are considered as solutions for detecting anomalies in images which are inherently non-stationary, i.e., the images contain more than one type of background. We conclude that a recent algorithm suggested by A. Schaum [1] is most successful when coupled with several variations which we suggest. In particular, in concurrence with Schaum, for pixels in transition zones between two neighboring stationary areas, it is crucial to choose or construct a covariance matrix which is appropriate for that particular area. Methods to choose both the sample covariance matrix and the estimated local mean will be discussed.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"27 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":"124546092","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: 18
The detailed vegetation classification for airborne hyperspectral remote sensing imagery by combining PCA and PP 基于PCA和PP的航空高光谱遥感影像植被精细分类
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594847
Zhang Lianpeng, L. Qinhuo, Zhao Changsheng, Lin Hui, Sun Huasheng
{"title":"The detailed vegetation classification for airborne hyperspectral remote sensing imagery by combining PCA and PP","authors":"Zhang Lianpeng, L. Qinhuo, Zhao Changsheng, Lin Hui, Sun Huasheng","doi":"10.1109/WHISPERS.2010.5594847","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594847","url":null,"abstract":"The feature extraction and dimensionality reduction is one of the core problems in hyperspectral remote sensing imagery processing. For the detailed vegetation classification, a projection index is established. It describes the separability of easy mixed classified vegetation objects. By optimizing the index, the projection directions may be calculated and the directions are orthogonal each other. The feature subspace of full data space may be constructed by combining principal components directions and the projection pursuit directions. The classification is completed on the feature subspace. It is hopeful to increase the classification accuracy especially the accuracy of easy mixed classified objects by the strategy. To verify the conclusion, a classification experiment is completed on an airborne hyperspectral imagery, the result shows that the overall classification accuracy promote 7% and the accuracy of easy mixed classified objects promote more than 20%.","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":"117336723","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
Martian aerosol abundance estimation based on unmixing of hyperspectral imagery 基于高光谱图像解混的火星气溶胶丰度估算
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594942
B. Luo, X. Ceamanos, S. Douté, J. Chanussot
{"title":"Martian aerosol abundance estimation based on unmixing of hyperspectral imagery","authors":"B. Luo, X. Ceamanos, S. Douté, J. Chanussot","doi":"10.1109/WHISPERS.2010.5594942","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594942","url":null,"abstract":"Classical linear unmixing approaches are not valid if the atmosphere and the aerosol are present in hyperspectral data, since the mixture model is no longer linear. In this paper, we present an iterative approach for estimating the abundance of aerosol based on unmixing of Martian hyperspectral data. On one hand, the results can provide the information on the aerosol of the Mars, which is very difficult to obtain. On the other hand, we can use the result to remove the effect of the aerosol on the original image and obtain more accurate linear unmixing results on the surface reflectance.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"29 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":"114203641","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
Plenary speaker 1: Continuum fusion: A new theory of inference 全体发言嘉宾1:连续统融合:一种新的推理理论
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594828
A. Schaum
{"title":"Plenary speaker 1: Continuum fusion: A new theory of inference","authors":"A. Schaum","doi":"10.1109/WHISPERS.2010.5594828","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594828","url":null,"abstract":"By exploiting human insight in the form of a model, methods of composite hypothesis (CH) testing can generate more robust decision algorithms, with a greater ability to generalize, than the alternative “data-driven methods.” The latter include artificial neural networks, genetic algorithms, support vector machines, etc.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"85 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":"123286525","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
Efficient combination of multiple hyperspectral data processing chains using binary decision trees 利用二叉决策树高效组合多个高光谱数据处理链
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594833
K. Bakos, P. Gamba
{"title":"Efficient combination of multiple hyperspectral data processing chains using binary decision trees","authors":"K. Bakos, P. Gamba","doi":"10.1109/WHISPERS.2010.5594833","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594833","url":null,"abstract":"According to the technical literature, there is no classification algorithm which is able to extract different classes with the same quality. In this paper we introduce a novel methodology to build a multi-stage, hierarchical data processing approach that is able to combine the advantages of different processing chains, which may be best suited for specific classes. The combination process is carried out using a binary decision tree (BDT) structure where at each node the most useful input information source, in the form of different processing chains, is used, according to the outcome of a simple learning mechanism on small training/validation subsets. Final results are instead achieved by applying the designed BDT to the whole data set. The usefulness of the procedure is proved by extensive analysis of a standard test data set, the Indian Pine AVIRIS set.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"15 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":"126310318","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
Classification for hyperspectral imagery based on sparse representation 基于稀疏表示的高光谱图像分类
2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594882
Yi Chen, N. Nasrabadi, T. Tran
{"title":"Classification for hyperspectral imagery based on sparse representation","authors":"Yi Chen, N. Nasrabadi, T. Tran","doi":"10.1109/WHISPERS.2010.5594882","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594882","url":null,"abstract":"A new sparsity-based classification algorithm for hyperspectral imagery is proposed in this paper. This algorithm is based on the assumption that the spectral signatures of pixels in the same class lie in a low-dimensional subspace and thus a test sample can be represented by a sparse linear combination of the training samples. The sparse representation is recovered by solving a constrained optimization and it directly determines the class label of the test sample. In addition to the constraints on sparsity and reconstruction accuracy, the smoothness of hyperspectral images across neighboring pixels is also exploited by forcing the Laplacian of the reconstructed image to be minimum in the optimization process. Various sparse recovery techniques are applied to solve the optimization problem and their performances are compared against the widely used Support Vector Machine classifier. Simulation results show that the proposed algorithm yields a favorable performance over the support vector machines.","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":"130451400","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: 27
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