IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003最新文献_第5页

Random forests of binary hierarchical classifiers for analysis of hyperspectral data 用于高光谱数据分析的二元分层分类器随机森林

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295213

M. Crawford, Jisoo Ham, Yangchi Chen, Joydeep Ghosh

{"title":"Random forests of binary hierarchical classifiers for analysis of hyperspectral data","authors":"M. Crawford, Jisoo Ham, Yangchi Chen, Joydeep Ghosh","doi":"10.1109/WARSD.2003.1295213","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295213","url":null,"abstract":"Statistical classification of hyperspectral data is challenging because the input space is high in dimension and correlated, but labeled information to characterize the class distributions is typically sparse. The resulting classifiers are often unstable and have poor generalization. A new approach that is based on the concept of random forests of classifiers and implemented within a multiclassifier system arranged as a binary hierarchy is proposed. The primary goal is to achieve improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited. The new classifier incorporates bagging of training samples and adaptive random subspace feature selection with the binary hierarchical classifier (BHC), such that the number of features that is selected at each node of the tree is dependent on the quantity of associated training data. Classification results from experiments on data acquired by the Hyperion sensor on the NASA EO-1 satellite over the Okavango Delta of Botswana are superior to those from our original best basis BHC algorithm, a random subspace extension of the BHC, and a random forest implementation using the CART classifier.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123722429","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: 28

Classification of polarimetric synthetic aperture radar images using fuzzy clustering 基于模糊聚类的极化合成孔径雷达图像分类

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295186

P. Kersten, J. Lee, T. Ainsworth, M. Grunes

引用次数: 2

Operational segmentation and classification of SAR sea ice imagery SAR海冰图像的业务分割与分类

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295204

David A Clausi, H. Deng

引用次数: 11

Methodology for hyperspectral band and classification model selection 高光谱波段和分类模型选择方法

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295183

P. Groves, P. Bajcsy

引用次数: 63

Machine learning approaches to multisource geospatial data classification: application to CRP mapping in Texas County, Oklahoma 多源地理空间数据分类的机器学习方法:在俄克拉荷马州德克萨斯县CRP制图中的应用

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295194

X. Song, F. Guoliang, M. Rao

{"title":"Machine learning approaches to multisource geospatial data classification: application to CRP mapping in Texas County, Oklahoma","authors":"X. Song, F. Guoliang, M. Rao","doi":"10.1109/WARSD.2003.1295194","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295194","url":null,"abstract":"We develop an Automated Feature Information Retrieval System (AFIRS) for accurate classification of multisource geospatial data, which involves multispectral Landsat imagery, ancillary geographic information system (GIS) data and other derived features. Two machine learning approaches, i.e., decision tree classifier (DTC) and support vector machine (SVM), are implemented as multisource geospatial data classifiers in the AFIRS. Specifically, we apply the AFIRS to the mapping of United States Department of Agriculture (USDA)'s Conservation Reserve Program (CRP) tracts in Texas County, Oklahoma. CRP is a nationwide program, and recently USDA announced payments of nearly $1.6 billion for new CRP enrollments. It is imperative to obtain accurate CRP maps for effective and efficient management and evaluation of the CRP program. However, most existing CRP maps are inaccurate and little work has been done to improve their accuracy. The proposed AFIRS is capable of handling the complex CRP mapping problem with high accuracy when limited training samples are available. Simulation results show that 5-10% improvements can be obtained by incorporating GIS ancillary data and other derived features in addition to multispectral imagery. This work validates the applicability of machine learning approaches to the complex real-world remote sensing applications.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121408841","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

Automatic registration of electro-optical and SAR images 自动配准电光和SAR图像

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295196

G. Lampropoulos, J. Chan, J. Secker, Y. Li, A. Jouan

引用次数: 5

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295179

James Norman Sweet

{"title":"The spectral similarity scale and its application to the classification of hyperspectral remote sensing data","authors":"James Norman Sweet","doi":"10.1109/WARSD.2003.1295179","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295179","url":null,"abstract":"Hyperspectral images have considerable information content and are becoming common. Analysis tools must keep up with the changing demands and opportunities posed by the new datasets. Many spectral image analysis algorithms depend on a scalar measure of spectral similarity or 'spectral distance' to provide an estimate of how closely two spectra resemble each other. Unfortunately, traditional spectral similarity measures are ambiguous in their distinction of similarity. Traditional metrics can define a pair of spectra to be nearly identical mathematically yet visual inspection shows them to be spectroscopically dissimilar. These algorithms do not separately quantify both magnitude and direction differences. Three common algorithms used to measure the distance between remotely sensed reflectance spectra are Euclidean distance, correlation coefficient, and spectral angle. Euclidean distance primarily measures overall brightness differences but does not respond to the correlation (or lack thereof) between two spectra. The correlation coefficient is very responsive to differences in direction (i.e. spectral shape) but does not respond to brightness differences due to band-independent gain or offset factors. Spectral angle is closely related mathematically to the correlation coefficient and is primarily responsive to differences in spectral shape. However, spectral angle does respond to brightness differences due to a uniform offset, which confounds the interpretation of the spectral angle value. This paper proposes the spectral similarity scale (SSS) as an algorithm that objectively quantifies differences between reflectance spectra in both magnitude and direction dimensions (i.e. brightness and spectral shape). Therefore, the SSS is a fundamental improvement in the description of distance or similarity between two reflectance spectra. In addition, it demonstrates the use of the SSS by discussing an unsupervised classification algorithm based on the SSS named ClaSSS.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116079974","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: 61

Optimum data transmission and imaging method for high resolution imaging from Earth observation satellite 对地观测卫星高分辨率成像的最佳数据传输与成像方法

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295188

R. Nagura

{"title":"Optimum data transmission and imaging method for high resolution imaging from Earth observation satellite","authors":"R. Nagura","doi":"10.1109/WARSD.2003.1295188","DOIUrl":"https://doi.org/10.1109/WARSD.2003.1295188","url":null,"abstract":"Recently, an enormous amount of progress has been made in remote sensing imaging, providing images in the ground pixel size resolution and spectral band width resolution. These imaging methods are very important and indispensable for the progress of remote sensing from space. With this progress, the data rates from satellites are extensively increasing. The typical value of the net data rate exceeds 1 Gbps, not including any synchronous code nor error correcting code. Therefore, the high performance data expression should be indispensable especially in the high resolution observation system. This paper reports the optimum transmission system using the data compression and the error correction code under the above circumstances. There are many kinds of data compression techniques, however we need accurate and high signal to noise ratio of compressed images for Earth observation. This paper mainly considers the JPEG2000 compression method. In the transmission of compressed data, the effects of bit error would be very important and sometimes fatally damages the image quality. Accordingly, error correction would be indispensable for high quality data transmission. The paper mainly discusses error correction using the Turbo code, and shows the effect of error disappearance in the receiving data of the compressed image. Finally, the paper proposes the time integration method and improvement of the signal to noise ratio of original images.","PeriodicalId":395735,"journal":{"name":"IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134292271","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

Learning Bayesian classifiers for a visual grammar 学习贝叶斯分类器的视觉语法

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295195

S. Aksoy, K. Koperski, C. Tusk, G. Marchisio, J. Tilton

引用次数: 2

Cluster-space classification: a fast k-nearest neighbour classification for remote sensing hyperspectral data 聚类空间分类:用于遥感高光谱数据的快速k近邻分类

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI: 10.1109/WARSD.2003.1295222

X. Jia, J. Richards

引用次数: 8