2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第10页

Exploiting spatial information in semi-supervised hyperspectral image segmentation 利用空间信息进行半监督高光谱图像分割

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594877

Jun Li, J. Bioucas-Dias, A. Plaza

{"title":"Exploiting spatial information in semi-supervised hyperspectral image segmentation","authors":"Jun Li, J. Bioucas-Dias, A. Plaza","doi":"10.1109/WHISPERS.2010.5594877","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594877","url":null,"abstract":"We present a new semi-supervised segmentation algorithm suited to hyperspectral images, which takes full advantage of the spectral and spatial information available in the scenes. We mainly focus on problems involving very few labeled samples and a larger set of unlabeled samples. A multinomial logistic regression (MLR) is used to model the posterior class probability distributions, whereas a multilevel logistic level (MLL) prior is adopted to model the spatial information present in class label images. The multinomial logistic regressors are learnt using an expectation maximization (EM) type algorithm, where the class labels of the unlabeled samples are dealt with as unobserved random variables. The expectation step of the EM algorithm is computed using belief propagation (BP). In the maximization step of the EM algorithm, we compute the maximum a posterioi estimate (MAP) estimate of the multinomial logistic regressors. For the segmentation, we compute both the MAP solution and the maxi-mizer of the posterior marginal (MPM) provided by the belief propagation algorithm. We show, using the well-known AVIRIS Indian Pines data, that both solutions exhibit state-of-the-art performance.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"14 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":"116752330","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

Derivation of stress severities in wheat from hyperspectral data using support vector regression 利用支持向量回归从高光谱数据推导小麦的胁迫程度

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594921

T. Mewes, B. Waske, J. Franke, G. Menz

{"title":"Derivation of stress severities in wheat from hyperspectral data using support vector regression","authors":"T. Mewes, B. Waske, J. Franke, G. Menz","doi":"10.1109/WHISPERS.2010.5594921","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594921","url":null,"abstract":"The benefits and limitations of crop stress detection by hyperspectral data analysis have been examined in detail. It could thereby be demonstrated that even a differentiation between healthy and fungal infected wheat stands is possible and profits by analyzing entire spectra or specifically selected spectral bands/ranges. For reasons of practicability in agriculture, spatial information about the health status of crop plants beyond a binary classification would be a major benefit. Thus, the potential of hyperspectral data for the derivation of several disease severity classes or moreover the derivation of continual disease severity has to be further examined. In the present study, a state-of-the-art regression approach using support vector machines (SVM) has been applied to hyperspectral AISA-Dual data to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat. Ground truth disease ratings were realized within an experimental field. A mean correlation coefficient of r=0.69 between severities and support vector regression predicted severities could be achieved using indepent training and test data. The results show that the SVR is generally suitable for the derivation of continual disease severity values, but the crucial point is the uncertainty in the reference severity data, which is used to train the regression.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"37 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":"121982293","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

A spatial/spectral protocol for quality assurance of decompressed hyperspectral data for practical applications 用于实际应用的解压缩高光谱数据质量保证的空间/光谱协议

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594855

A. Brook, E. Ben-Dor

引用次数: 2

The Physics of spectral invariants 谱不变量的物理学

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594910

M. Mõttus

引用次数: 2

Monitoring heathland habitat status using hyperspectral image classification and unmixing 利用高光谱图像分类与解混监测荒原生境状况

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594895

S. Delalieux, B. Somers, B. Haest, L. Kooistra, C. A. Mücher, J. V. Borre

{"title":"Monitoring heathland habitat status using hyperspectral image classification and unmixing","authors":"S. Delalieux, B. Somers, B. Haest, L. Kooistra, C. A. Mücher, J. V. Borre","doi":"10.1109/WHISPERS.2010.5594895","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594895","url":null,"abstract":"Natura 2000, an EU-wide network of nature protection areas, has as main objective the achievement or maintenance of a favorable conservation status of habitats protected by the EU Habitats directives. Within this framework, this study examines a strategy to characterize the status of heathland vegetation from airborne hyperspectral AHS data in the Kalmthoutse Heide, Flanders, Belgium. A hierarchical classification scheme was set-up with the highest detail focusing on vegetation structural elements that determine the conservation status of the habitat. Although conventional classification algorithms performed very well (accuracies > 90%) in discriminating broad land cover classes and habitat types (level 1 to 3), they failed in accurately distinguishing different heather age classes which are an important indicator for the structural quality of the heathland habitat (level 4). Since all heather life stages have their specific structural characteristics, a subpixel unmixing approach succeeded by a decision tree classification was implemented to map variations in heathland morphology and as such enhance the ecological value of information derived from remote sensing data.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"37 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":"123008674","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: 9

Fusion of hyperspectral images and LiDAR data for civil engineering structure monitoring 用于土木工程结构监测的高光谱图像与激光雷达数据融合

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594872

A. Brook, E. Ben-Dor, R. Richter

{"title":"Fusion of hyperspectral images and LiDAR data for civil engineering structure monitoring","authors":"A. Brook, E. Ben-Dor, R. Richter","doi":"10.1109/WHISPERS.2010.5594872","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594872","url":null,"abstract":"Investigation of civil engineering materials includes a wide range of applications that requires three-dimensional (3D) information. Complex structures shapes and formations within heterogeneous artificial/natural land covers under varying environmental conditions requires knowledge on the 3D status of the urban materials for better (visual) interpretation of polluted sources. Obtaining 3D information and merge them with aerial photography is not a trivial task. It is thus, strongly needed to develop new approaches for near real time analysis of the urban environment with natural 3D visualization of extensive coverage. The hyperspectral remote sensing (HRS) technology is a promising and powerful tool to assess degradation of urban materials in artificial structures by exploring possible chemical physical changes using spectral information across the VIS-NIR-SWIR spectral region (400–2500nm). This technique provides the ability for easy, rapid and accurate in situ assessment of many materials on a spatial domain within near real time condition and high temporal resolution. LiDAR technology, on the other hand, offers precise information about the geometrical properties of the surfaces within the study areas and can reflect different shapes and formations of the complex urban environment. Generating a monitoring system that is based on the integrative fusion between HRS and LiDAR data may enlarge the application envelop of each technology separately and contribute valuable information on urban runoff and planning. The aim of the presented research is to implement this direction and define set of rules for practical integration between the two datasets. A fusion process defined by integrative decision tree analysis includes spectral/spatial and 3D information is developed and presented.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"7 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":"130027473","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: 25

Above and in-water measurements of reflectance and chlorophyll algorithms in the Kaštela Bay in the Adriatic Sea 亚得里亚海Kaštela湾的水面和水中反射率和叶绿素算法测量

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594876

M. Kisevic, M. Morovic, A. Smailbegovic, Koko Anaricevic

引用次数: 2

Improved change detection through post change classification: A case study using synthetic hyperspectral imagery 通过变化后分类改进变化检测:一个使用合成高光谱图像的案例研究

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594930

Karmon Vongsy, M. Mendenhall

引用次数: 4

Unsupervised clustering of soil spectral curves to obtain their stronger correlation with soil properties 对土壤光谱曲线进行无监督聚类，以获得其与土壤性质更强的相关性

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594852

J. Cierniewski, Cezary Kaźmierowski, K. Kusnierek, J. Piekarczyk, Slawomir Kwlewicz, Marcin Gulinski, H. Terelak, T. Stuczynski, B. Maliszewska-Kordybach

{"title":"Unsupervised clustering of soil spectral curves to obtain their stronger correlation with soil properties","authors":"J. Cierniewski, Cezary Kaźmierowski, K. Kusnierek, J. Piekarczyk, Slawomir Kwlewicz, Marcin Gulinski, H. Terelak, T. Stuczynski, B. Maliszewska-Kordybach","doi":"10.1109/WHISPERS.2010.5594852","DOIUrl":"https://doi.org/10.1109/WHISPERS.2010.5594852","url":null,"abstract":"The laboratory measurements of the diffuse spectral reflectance of 212 soil samples, representing many various taxonomic units, collected throughout the area of arable lands in Poland, were conducted to investigate the relationship between the soil reflectance and their selected properties. It was found that among various tested transformations the first derivative of the soil reflectance was the one most strongly correlated with the content of textural fractions, soil organic carbon, Fe and CaCO3, The use of unsupervised Ward's Euclidian distance based on clustering algorithm to split the total dataset into subsets, according to the shape and the level of the soil spectra, improved the correlation between soil properties and the transformed spectral data. The highest values of the coefficient of determination R2 for clay and Fe contents on the total dataset reached only 0.64 and 0.56, respectively. Using the ED Ward's algorithm, six subsets were formed and their R2 increased up to 0.87 and 0.80, respectively.","PeriodicalId":193944,"journal":{"name":"2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"2 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":"115429804","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

Sea bed maping in the lagoon of New Caledonia 新喀里多尼亚泻湖的海床制图

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2010-06-14 DOI: 10.1109/WHISPERS.2010.5594846

A. Minghelli-Roman, C. Dupouy, C. Chevillon, P. Douillet

引用次数: 0