2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第3页

Hyperspectral remote sensing data to map hazardous materials in a rural and industrial district: The Podgorica dwellings case studies 用高光谱遥感数据绘制农村和工业区有害物质图:波德戈里察住宅案例研究

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289026

R. Cavalli, S. Pascucci, S. Pignatti

{"title":"Hyperspectral remote sensing data to map hazardous materials in a rural and industrial district: The Podgorica dwellings case studies","authors":"R. Cavalli, S. Pascucci, S. Pignatti","doi":"10.1109/WHISPERS.2009.5289026","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289026","url":null,"abstract":"In this paper, we present the results of a hyperspectral airborne and in situ campaign in Montenegro aimed at individuating and monitoring two hazardous materials. They are the residues of the bauxite processing, i.e. red mud, and the asbestos fibers applied in the building materials. We perform laboratory analyses of asbestoscement, red mud and soil samples collected in the study area for (a) recognizing the dominant minerals using XRay Diffraction and X-Ray Fluorescence; (b) identifying the optical characteristics of the samples using a portable field spectrometer; and (c) characterizing their spectral features and remote sensing detection requirements. A least-squares fitting procedure, on the basis of the significant red mud and asbestos-cement reflectance spectral features, was applied to airborne hyperspectral remote sensing data collected over the study area. Results show that hyperspectral remote sensing data can provide an efficient, fast and repeatable tool for mapping and monitoring the diffusion of pollutants providing the location of the hazardous areas to be checked.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128923654","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: 8

Hyperspectral unmixing from a convex analysis and optimization perspective 从凸分析和优化角度的高光谱解混

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289018

Tsung-Han Chan, Wing-Kin Ma, Chong-Yung Chi, A. Arulmurugan

{"title":"Hyperspectral unmixing from a convex analysis and optimization perspective","authors":"Tsung-Han Chan, Wing-Kin Ma, Chong-Yung Chi, A. Arulmurugan","doi":"10.1109/WHISPERS.2009.5289018","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289018","url":null,"abstract":"In hyperspectral remote sensing, unmixing a data cube into spectral signatures and their corresponding abundance fractions plays a crucial role in analyzing the mineralogical composition of a solid surface. This paper describes a convex analysis perspective to (unsupervised) hyperspectral unmixing. Such an endeavor is not only motivated by the recent prevalence of convex optimization in signal processing, but also by the nature of hyperspectral unmixing (specifically, non-negativity and full additivity of abundances) that makes convex analysis a very suitable tool. By the notion of convex analysis, we formulate two optimization problems for solving hyperspectral unmixing, which have the intuitive ideas following the works by Craig and Winter respectively but adopt an optimization treatment different from those previous works. We show the connection of the two hyperspectral unmixing optimization problems, by proving that their optimal solutions become identical when pure pixels exist in the data. We also illustrate how the two problems can be conveniently handled by alternating linear programming. Monte Carlo simulations are presented to demonstrate the efficacy of the two hyperspectral unmixing formulations.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122354841","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

Improved hyperspectral anomaly detection in heavy-tailed backgrounds 改进的重尾背景高光谱异常检测

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289019

S. Adler-Golden

引用次数: 20

The structure of the APEX (airborne prism experiment) Processing and Archiving Facility 机载棱镜实验处理与存档设备的结构

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289106

A. Hueni, J. Bieseman, F. Dell'Endice, E. Alberti, K. Meuleman, M. Schaepman

引用次数: 7

Multiple instance and context dependent learning in hyperspectral data 高光谱数据中的多实例和上下文依赖学习

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289021

P. Torrione, Christopher R. Ratto, L. Collins

{"title":"Multiple instance and context dependent learning in hyperspectral data","authors":"P. Torrione, Christopher R. Ratto, L. Collins","doi":"10.1109/WHISPERS.2009.5289021","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289021","url":null,"abstract":"Hyperspectral imaging (HSI) is a powerful tool for various remote sensing tasks including agricultural modeling and landmine/ unexploded ordnance clearance. Although the application of standard supervised learning techniques to HSI data has previously been explored, several aspects of hyperspectral data collection and ground truth labeling make some of the assumptions underlying standard machine learning techniques invalid. For example, HSI is highly dependent upon local environmental conditions, and pixel-by-pixel labels for HSI data are often not available. As a result, data from hyperspectral sensing under various scenarios is not typically i.i.d., and correct data labels must be inferred from training data while learning decision boundaries. In this work we explore two possible solutions to these problems: context-dependent learning for overcoming variations between collections, and multiple instance learning for simultaneously inferring local target labels and global target decision boundaries. Results are compared to standard logistic discriminant classification approaches.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132409001","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

4D deconvolution and demixing for supernova follow-up 超新星跟踪的4D反褶积和去混

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289034

S. Bongard, É. Thiébaut, F. Soulez, E. Pecontal

引用次数: 0

Comparison of radiative transfer in physics-based models for an improved understanding of empirical hyperspectral data 基于物理模型的辐射传输比较，以提高对经验高光谱数据的理解

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288986

S. Matteoli, Emmett Ientilucci, J. Kerekes

{"title":"Comparison of radiative transfer in physics-based models for an improved understanding of empirical hyperspectral data","authors":"S. Matteoli, Emmett Ientilucci, J. Kerekes","doi":"10.1109/WHISPERS.2009.5288986","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288986","url":null,"abstract":"This paper examines the methodology of detecting targets in airborne or satellite hyperspectral imagery using physicsbased models. More specifically, the radiative transfer inherently coupled to various physical models is considered. In fact, taking into account atmospheric effects is crucial in target detection applications, especially when dealing with targets that are particularly difficult to detect. Many tools have been developed independently which incorporate physical models that simulate atmospheric radiation transfer. Some (e.g. DIRSIG) predict sensor-reaching radiance while others (e.g. FLAASH, ATREM) retrieve ground-leaving reflectance by removing atmospheric effects. With the final aim of performing forward modeling target detection on a particularly challenging scenario, this paper illustrates the preliminary study carried out in order to assess the physical model employed and achieve a better data understanding before proceeding to detection. A cross-comparison between some well-known and established models, in addition to forward modeling, is examined. Results reveal the need for better understanding of real data by identifying the major sources of uncertainty. The strong impact of atmospheric condition uncertainty and adjacency effects, along with, though to a lesser extent, further inaccuracy introduced by possible calibration and spectral library measurement errors, are all factors that will be investigated in future work.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123463324","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

Morphological scale-space for hyperspectral images and dimensionality exploration using tensor modeling 高光谱图像的形态尺度空间和使用张量建模的维度探索

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289059

S. Velasco-Forero, J. Angulo

引用次数: 9

Functional and pathological analysis of biological systems using vibrational spectroscopy with chemometric and heuristic approaches 利用化学计量学和启发式方法的振动光谱学对生物系统进行功能和病理分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288989

A. Meade, C. Clarke, F. Bonnier, K. Poon, Amaya Garcia, P. Knief, K. Ostrowska, L. Salford, H. Nawaz, F. Lyng, H. Byrne

{"title":"Functional and pathological analysis of biological systems using vibrational spectroscopy with chemometric and heuristic approaches","authors":"A. Meade, C. Clarke, F. Bonnier, K. Poon, Amaya Garcia, P. Knief, K. Ostrowska, L. Salford, H. Nawaz, F. Lyng, H. Byrne","doi":"10.1109/WHISPERS.2009.5288989","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5288989","url":null,"abstract":"Vibrational spectroscopy (Raman and FTIR microspectroscopy) is an attractive modality for the analysis of biological samples since it provides a complete non-invasive acquisition of the biochemical fingerprint of the sample. Studies in our laboratory have applied vibrational spectroscopy to the analysis of biological function in response to external agents (chemotherapeutic drugs, ionising radiation, nanoparticles), together with studies of the pathology of tissue (skin and cervix) in health and disease. Genetic algorithms have been used to optimize spectral treatments in tandem with the analysis of the data (using generalized regression neural networks (GRNN), artificial neural networks (ANN), partial least squares modelling (PLS), and support vector machines (SVM)), to optimize the complete analytical scheme and maximize the predictive capacity of the spectroscopic data. In addition we utilise variable selection techniques to focus on spectral features that provide maximal classification or regression of the spectroscopic data against analytical targets. This approach has yielded interesting insights into the variation of biochemical features of the biological system with its state, and has also provided the means to develop further the analytical and predictive capabilities of vibrational spectroscopy in biological analysis.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126330208","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 agricultural crops with EO-1 Hyperion data 利用EO-1 Hyperion数据绘制农作物图

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289057

K. Ntouros, I. Gitas, Georgios N. Silleos

{"title":"Mapping agricultural crops with EO-1 Hyperion data","authors":"K. Ntouros, I. Gitas, Georgios N. Silleos","doi":"10.1109/WHISPERS.2009.5289057","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289057","url":null,"abstract":"Hyperspectral data acquired by the Hyperion instrument, on board the Earth Observing - 1 Satellite (EO-1), were evaluated for the classification of five agricultural crops (maize (Zea mays), cotton (Gossypium hirsutum L.), rice (Oryza Sativa), tobacco (Nicotiana Tabacum) and tomato (Lycopersicon esculentum)) in Greece and the results were compared to classification of Landsat 5 TM data. In addition, was investigated the contribution of Hyperion SWIR bands on crops classification. The research was conducted in an agricultural area located in the North-Eastern Greece. The Hyperion radiance values, from the 196 bands, were converted into surface reflectance values using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model which is embedded in ENVI software. The data dimensionality reduction of Hyperion's image bands was achieved by using MNF transformation whereas the Maximum Likelihood algorithm was used in order to perform image data classification. The results showed that an overall accuracy of 91% was obtained from the classification of Hyperion image, while the overall accuracy resulted from the classification of Landsat 5 TM image was 81%. Also the Hyperion SWIR bands provide additional information on crops classification, not available by VNIR bands.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126481876","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