2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing最新文献_第7页

Classification of hyperspectral images using automatic marker selection and Minimum Spanning Forest 基于自动标记选择和最小生成森林的高光谱图像分类

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289054

Y. Tarabalka, J. Chanussot, J. Benediktsson

{"title":"Classification of hyperspectral images using automatic marker selection and Minimum Spanning Forest","authors":"Y. Tarabalka, J. Chanussot, J. Benediktsson","doi":"10.1109/WHISPERS.2009.5289054","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289054","url":null,"abstract":"A new method for segmentation and classification of hyperspectral images is proposed. The method is based on the construction of a Minimum Spanning Forest (MSF) from region markers. Markers are defined automatically from classification results. For this purpose, pixel-wise classification is performed and the most reliable classified pixels are chosen as markers. Furthermore, each marker defined from classification results is associated with a class label. Each tree in the MSF grown from a marker forms a region in the segmentation map. By assigning a class of each marker to all the pixels within the region grown from this marker, classification map is obtained. Furthermore, the classification map is refined, using results of a pixel-wise classification and a majority voting within the spatially connected regions. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's Indian Pine site. The use of different dissimilarity measures for construction of the MSF is investigated. The proposed scheme improves classification accuracies, when compared to previously proposed classification techniques, and provides accurate segmentation and classification maps.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"47 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":"116986871","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

On the modeling and processing of polarization in Raman spectroscopy 拉曼光谱偏振的建模与处理

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289083

S. Miron, D. Brie, M. Dossot, B. Humbert

引用次数: 1

Building an integumentary system hyperspectral model for avatars 建立虚拟角色的皮肤系统高光谱模型

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289038

Abel S. Nunez, M. Mendenhall, Heidi C. Bertram, A. Brooks

引用次数: 2

Local-global background modeling for anomaly detection in hyperspectral images 高光谱图像异常检测的局部-全局背景建模

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289036

E. Madar, O. Kuybeda, D. Malah, M. Barzohar

引用次数: 11

Multispectral imaging the sun in the ultraviolet 用紫外线对太阳进行多光谱成像

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288994

T. D. D. Wit, S. Moussaoui, P. Amblard, J. Aboudarham, F. Auchère, M. Kretzschmar, J. Lilensten

引用次数: 0

Improvement of remote sensing multispectral image classification by using Independent Component Analysis 独立分量分析在遥感多光谱图像分类中的改进

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289033

M. S. Karoui, Y. Deville, S. Hosseini, A. Ouamri, D. Ducrot

{"title":"Improvement of remote sensing multispectral image classification by using Independent Component Analysis","authors":"M. S. Karoui, Y. Deville, S. Hosseini, A. Ouamri, D. Ducrot","doi":"10.1109/WHISPERS.2009.5289033","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289033","url":null,"abstract":"This paper deals with the application of Independent Component Analysis (ICA) as a solution to Blind Source Separation (BSS), in order to pre-process remote sensing multispectral images before we classify them. We analyze the structure of the considered data, and especially show that each recorded image corresponding to a spectral band may be seen as an observation consisting of a mixture (linear combination) of source images. The latter images correspond to the abundances of the pure elements (endmembers) in the pixels. Using BSS methods, one can hope to reduce the mixing effect in these observations, which then allows better recognition of the classes constituting the observed scene. Based on this approach, we create new images (i.e. at least partly separated images) by using ICA, starting from HRV SPOT images. These images are then used as inputs of a supervised classifier integrating textural information. The separated image classification results show a clear improvement compared to classification of initial images. This show the contribution of ICA as an attractive pre-processing for classification of multispectral remote sensing imagery.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"5 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":"126942383","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: 11

Estimating foliar biochemistry from reflectance and the detection of phellinus sulphurascens induced stress 利用叶片反射率估算叶片生物化学及硫黄诱导胁迫的检测

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5288991

G. Quinn, K. Niemann, D. Goodenough

引用次数: 5

Kernel subspace-based anomaly detection for hyperspectral imagery 基于核子空间的高光谱图像异常检测

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289028

N. Nasrabadi

引用次数: 4

LiDAR-guided analysis of airborne hyperspectral data 激光雷达制导机载高光谱数据分析

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289029

K. Niemann, G. Frazer, R. Loos, F. Visintini

引用次数: 8

Preliminary hyperspectral band selection for difficult object detection 高难目标探测的初步高光谱波段选择

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Pub Date : 2009-10-16 DOI: 10.1109/WHISPERS.2009.5289055

Lukasz Paluchowski, P. Walczykowski

{"title":"Preliminary hyperspectral band selection for difficult object detection","authors":"Lukasz Paluchowski, P. Walczykowski","doi":"10.1109/WHISPERS.2009.5289055","DOIUrl":"https://doi.org/10.1109/WHISPERS.2009.5289055","url":null,"abstract":"Automatic target detection has been a well-known topic since the early 1990's. With the development of digital photographic techniques it has became even more popular. So far, a lot of algorithms for artificial objects detection from natural backgrounds have been elaborated and developed. Unfortunately detecting difficult objects like military, camouflaged targets is still a hard task and it leads in many times to a big number of false alarms. The objective of this paper is to provide a comparative analysis of methods for object detection based on single hyperspectral band, two-band and multiple band. In this studies, to check the spectral contrast between objects and background, the algorithm based on mahalanobis distance has been used. All possible two-band and three band combinations have been checked and compared with single band. We analyzed data coming from hyperspectral ground based system consists of digital video camera and optoelectronic tuneable filter. Data was collected mostly in near infrared range with 10nm spectral resolution. Additional we took a challenge to classify the methods taking into account possibility of unknown object detection and detecting object with known spectral characteristic. Results obtained are interesting. They point to a need of proper band selection for difficult object detection and form the basis for the expansion of research.","PeriodicalId":242447,"journal":{"name":"2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing","volume":"41 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":"123349663","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