2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第4页

Independent Component Analysis for coastal water mapping using hyperspectral datasets 利用高光谱数据集进行沿海水域制图的独立分量分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289048

V. Karathanassi, P. Kolokoussis, Ioannidou Styliani

{"title":"Independent Component Analysis for coastal water mapping using hyperspectral datasets","authors":"V. Karathanassi, P. Kolokoussis, Ioannidou Styliani","doi":"10.1109/WHISPERS.2009.5289048","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289048","url":null,"abstract":"Independent Component Analysis (ICA) is considered to be one of the most recent and successful ways to produce independent components out of the hyperspectral cube. The tool tries to resolve the Blind Source Separation (BSS) statistical problem and has been applied to various case studies of hyperspectral datasets, for dimensionality reduction and separation of independent signal sources, i.e. endmembers. Many ICA algorithms have been proposed in the literature. In this study, the FastICA, JADE, BSS SVD, SONS, NG-OL, and SIMBEC algorithms were applied on airborne hyperspectral data for coastal water mapping. Emphasis was given on water turbidity. In order to enforce the capacities of FastICA, a methodology including the eigen-thresholding Harsanyi-Farrand-Chang noise suppression technique, as well as, three-level Discrete Wavelet Transform (DWT) was developed. Results were compared and evaluated with in situ measurements related to turbidity. ICA algorithms produced quite interesting results. The BSS SVD algorithm was proven the most efficient tool for coastal water mapping.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"284 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":"116100473","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

Development of a broad landscape monitoring system using hyperspectral imagery to detect pest infestation 开发利用高光谱图像检测害虫侵扰的广阔景观监测系统

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289005

J. Glaser, J. Casas, K. Copenhaver, Steffen Mueller

引用次数: 4

Adaptive nonparametric weighed feature extraction for hyperspectral image classification 自适应非参数加权特征提取用于高光谱图像分类

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288979

Bor-Chen Kuo, Shih-Syun Lin, Hsin-Hua Ho, Jinn-Min Yang

引用次数: 2

Automated labeling of segmented hyperspectral imagery via spectral matching 通过光谱匹配实现分割高光谱图像的自动标注

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289092

B. Bue, E. Merényi, B. Csathó

引用次数: 7

A novel scheme for the compression and classification of hyperspectral images 一种新的高光谱图像压缩与分类方法

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289075

B. Xie, T. Bose, E. Merényi

引用次数: 3

From preprocessing to fuzzy classification of IR images of paraffin embedded cancerous skin samples 从石蜡包埋癌样红外图像预处理到模糊分类

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289025

David Sebiskveradze, Elodie Ly, C. Gobinet, O. Piot, M. Manfait, P. Jeannesson, V. Vrabie

{"title":"From preprocessing to fuzzy classification of IR images of paraffin embedded cancerous skin samples","authors":"David Sebiskveradze, Elodie Ly, C. Gobinet, O. Piot, M. Manfait, P. Jeannesson, V. Vrabie","doi":"10.1109/WHISPERS.2009.5289025","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289025","url":null,"abstract":"Mid-Infrared (IR) micro-spectral imaging is an efficient method to analyze molecular composition of biomedical samples. In clinical oncology, this non-invasive technique is generally used on frozen biopsies to localize and diagnose cancerous tissues in their early stages. However, samples are usually fixed in paraffin in order to be preserved from decay, but the IR signature of paraffin prevents the study of the underlying tissue. To neutralize the paraffin signal from the recorded data, preprocessing methods based on Independent Component Analysis (ICA) and Nonnegatively Constrained Least Squares (NCLS) or on Extended Multiplicative Signal Correction (EMSC) have been recently developed. Then, in order to identify tumor areas, clustering techniques are applied on the preprocessed data, the final result being a false-color map of the biomedical sample which is comparable to the conventional histological image. By allowing each recorded spectrum to be assigned to every cluster, the fuzzy clustering gives more realistic results for unclear tissue boundaries by better highlighting the tumor and peritumoral areas. A recent algorithm based on the redundancy of classes allows to automatically estimate the optimal number of classes and the optimal fuzzy parameter. In this paper, we analyze the effects of the preprocessing methods on the optimal parameter extraction and on the results of the fuzzy clustering on different paraffin embedded cancerous skin samples.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"21 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":"122813871","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

Local approach to orthogonal subspace-based target detection in hyperspectral images 高光谱图像中基于正交子空间的局部目标检测方法

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289095

S. Matteoli, N. Acito, M. Diani, G. Corsini

{"title":"Local approach to orthogonal subspace-based target detection in hyperspectral images","authors":"S. Matteoli, N. Acito, M. Diani, G. Corsini","doi":"10.1109/WHISPERS.2009.5289095","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289095","url":null,"abstract":"Airborne or satellite hyperspectral sensing has proven valuable in many target detection applications, thanks to the dense spectral sampling of the sensed data, which provides a high material discriminability. Within this framework, this paper focuses on detection algorithms that rely upon subspace-based characterization of background. Whereas background subspace estimation has been typically accomplished through a global approach, which employs the whole image, a local methodology is here adopted. In fact, most of the interference affecting targets derives from the background materials in which they are inserted. Such a background interference lies in a subspace that is more likely spanned by the spectra of the pixels in the target neighborhood, rather than by endmembers/eigenvectors extracted from the whole image. Real hyperspectral imagery from the HyMap sensor is used to experimentally compare both global and local approaches to background subspace estimation. On this data, which exemplifies a mixed-pixel cluttered detection problem, detection results were strongly in favor of the local approach.","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":"131312031","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: 13

Independent Component Analysis of the Gamma Ray Spectrometer data of SELENE (Kaguya) SELENE (Kaguya)伽马射线谱仪数据的独立分量分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289071

O. Forni, O. Gasnault, B. Diez, C. d'Uston, S. Maurice, N. Hasebe, O. Okudaira, N. Yamashita, S. Kobayashi, Y. Karouji, M. Hareyama, M. Kobayashi, R. Reedy, K. Kim

{"title":"Independent Component Analysis of the Gamma Ray Spectrometer data of SELENE (Kaguya)","authors":"O. Forni, O. Gasnault, B. Diez, C. d'Uston, S. Maurice, N. Hasebe, O. Okudaira, N. Yamashita, S. Kobayashi, Y. Karouji, M. Hareyama, M. Kobayashi, R. Reedy, K. Kim","doi":"10.1109/WHISPERS.2009.5289071","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289071","url":null,"abstract":"We analyze the spectra measured by the Gamma Ray Spectrometer (GRS) on board the SELENE satellite orbiting the Moon. The spectra consist in 8192 energy channels ranging from 0 to 12 MeV and exhibiting lines of interest (O, Mg, Al, Si, Ti, Ca, Fe, K, Th, and U) superposed on a continuum. We have also analysed the data with various multivariate techniques, one of them being the Independent Component Analysis. We have used the JADE algorithm for our analysis that we focused in the energy range from 750 to 3000 keV. We identify at least three meaningful components. The first one is correlated to the Thorium map. The corresponding correlation coefficient spectrum exhibits the lines of Thorium, Potassium and Uranium. The second component is clearly correlated with the Iron as shown on its corresponding spectrum. Finally the third component seems to be related to the altitude of the spacecraft. This work shows that maps of elements such as iron will be available with the GRS data by a purely statistical analysis.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"353 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":"116353628","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

Discrimination of remnant tree species and regeneration stages in Queensland, Australia using hyperspectral imagery 利用高光谱影像对澳大利亚昆士兰州剩余树种和再生阶段的鉴别

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288981

A. Apan, S. Phinn, T. Maraseni

引用次数: 7

Edge detection on hyperspectral imagery via Manifold techniques 基于流形技术的高光谱图像边缘检测

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288984

Yuan Zhou, Bo Wu, Deren Li, Rongxing Li

引用次数: 14