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Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery

INTERNATIONAL JOURNAL OF REMOTE SENSING 2014年第10期35卷 3556-3578页

作者： Sekovski, Ivan Stecchi, Francesco Mancini, Francesco Del Rio, Laura Univ Cadiz Dept Earth Sci CASEM Puerto Real 11510 Spain Univ Bologna Dept Biol Geol & Environm Sci I-48123 Ravenna Italy Tech Univ Bari DICATECh I-70125 Bari Italy

Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. The monitoring of such environments could benefit from the semi-automatic shoreline delineation method, especially in terms of time, cost, and labour-intensiveness. This article explores the potential of using a semi-automatic approach in delineating a proxy-based shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. We studied the potential and differences of basic and easily accessible standard classification methods for shoreline detection. In particular we explored the use of high spatial and spectral resolution satellite imagery for shoreline extraction. The case study was carried out on a 40km coastal stretch facing the Northern Adriatic Sea (Italy) and belonging to the Municipality of Ravenna. In this area a frequent monitoring of shoreline position is required because of the extreme vulnerability to erosion phenomena that have resulted in a general trend of coastal retreat over recent decades. The wet/dry shorelines were delineated between the classes of wet and dry sand, resulting from different supervised (Parallelepiped, Gaussian Maximum Likelihood, Minimum-Distance-to-Means, and Mahalanobis distance) image classification techniques and the unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA). In order to assign reliability to outcomes, the extrapolated shorelines were compared to reference shorelines visually identified by an expert, by assessing the average mean distance between them. In addition, the correlation between offset rates and different types of coast was investigated to examine the influence of specific coastal features on shoreline extraction capability. The results highlighted a high level of compatibility. The average median distance between reference shorelines and those resulting from the classification methods was less than 5.6m (M

关键词： dry sand coastal environment multispectral imagery shorelines shore features Lidar LIght Detection and Ranging Beach erosion High resolution imaging classification methods Satellite Imagery

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Land-cover classification methods for multi-year AVHRR data

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2001年第8期22卷 1479-1493页

作者： Liang, S Univ Maryland Dept Geog Lab Global Remote Sensing Studies College Pk MD 20742 USA

Advanced Very High Resolution Radiometer (AVHRR) data have been extensively used for global land-cover classification, but few studies have taken direct and full advantage of the multi-year properties of AVHRR data. This study focused on generating effective classification features from multi-year AVHRR data to improve classification accuracy. Three types of features were derived from 12-year monthly composite normalized difference vegetation index (NDVI) and channel 4 brightness temperature from the NOAA/NASA Pathfinder AVHRR Land data for land-cover classification. The first is based on the shape of the annual average NDVI or brightness-temperature profile, which was then approximated by a Fourier series. The coefficients estimated by the weighted least-squares method were used for classification. The second and third features were based on the raw periodogram of the time series and the auto-regressive modelling. A global land-cover training database created from Landsat Thematic Mapper and Multi-spectral Scanner imagery was used for training and validation. Both quadrature discriminate analysis (QDA) and linear discriminate analysis (LDA) were explored for classification, and results indicate that QDA performs much better than LDA. The first feature, based on the mean annual shape, produced much better results than the other two features. It was also found that NDVI signals worked better than brightness-temperature signals. That is probably because top-of-atmosphere signals were used, and atmospheric contaminations significantly disturb the thermal signals and correlation structures of different cover types. Further validations are needed.

关键词： Time series Normalized Difference Vegetation Index (ndvi) Landsat Thematic mapper Atmospheric emissions signals brightness temperature classification methods QUADRATURE normalized difference vegetation index Fourier series

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A comparative assessment of classification methods

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DECISION SUPPORT SYSTEMS 2003年第4期35卷 441-454页

作者： Kiang, MY Calif State Univ Long Beach Coll Business Adm Dept Informat Syst Long Beach CA 90840 USA

classification systems play an important role in business decision-making tasks by classifying the available information based on some criteria. The objective of this research is to assess the relative performance of some well-known classification methods. We consider classification techniques that are based on statistical and AI techniques. We use synthetic data to perform a controlled experiment in which the data characteristics are systematically altered to introduce imperfections such as nonlinearity, multicollinearity, unequal covariance, etc. Our experiments suggest that data characteristics considerably impact the classification performance of the methods. The results of the study can aid in the design of classification systems in which several classification methods can be employed to increase the reliability and consistency of the classification. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： classification methods neural networks machine learning

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Assessment the performance of classification methods in water quality studies, A case study in Karaj River

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2015年第9期187卷 1页

作者： Sakizadeh, Mohamad Shahid Rajaee Teacher Training Univ Fac Sci Dept Environm Sci Tehran Iran

To show the performance of classification methods in water quality studies, linear discriminant, and Naive Bayesian classification methods were applied at nine sampling stations with respect to four parameters including COD, nitrite, nitrate, and total coliforms (selected from ten water quality variables) in Karaj River, Iran. To fulfill the goals of this study, the sampling stations were first separated into two groups using cluster analysis. Rural wastewater was the main source of pollution in the first group, whereas the quality of water in the second group has been degraded mainly by organic and agricultural pollution. In order to have an independent group against which the performance of other classification methods is considered, three cross-validation methods including twofold, leave-one-out, and holdout methods were utilized to retain an independent test set. The results of cross-validation for the linear discriminant analysis show that, except for the leave-one-out method with 11.1 % misclassification error, the overall performance has been the same as that of the training data set. Therefore, it has outperformed compared with that of Naive Bayesian classification method. However, even though in situations where the correlation coefficient among the parameters is low, the latest method can offer the same performance as that of linear discriminant analysis as well. A sensitivity analysis was implemented using ten water quality variables (pH, COD, EC, TDA, turbidity, nitrate, nitrite, sulfate, TC, and FC) to find the most important variables in the classification of Karaj River showing that turbidity, next to COD, pH, nitrate, and sulfate, have had the most contribution in this field.

关键词： classification methods Cluster analysis Water quality Sensitivity analysis

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A survey of image classification methods and techniques for improving classification performance

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2007年第5期28卷 823-870页

作者： Lu, D. Weng, Q. Indiana Univ Ctr Study Insts Populat & Environm Change Bloomington IN 47408 USA Indiana State Univ Dept Geog Geol & Anthropol Terre Haute IN 47809 USA

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

关键词： Literature Review remote-sensing data Image classification methods and techniques geographic information systems classification methods Classifiers

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Relevance Vector Machines: Sparse classification methods for QSAR

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2015年第8期55卷 1529-1534页

作者： Burden, Frank R. Winkler, David A. CSIRO Mfg Flagship Clayton Vic 3169 Australia Monash Inst Pharmaceut Sci Parkville Vic 3052 Australia Latrobe Inst Mol Sci Bundoora Vic 3083 Australia Flinders Univ S Australia Sch Chem & Phys Sci Bedford Pk SA 5042 Australia

Sparse machine learning methods have provided substantial benefits to quantitative structure property modeling, as they make model interpretation simpler and generate models with improved predictivity. Sparsity is usually induced via Bayesian regularization using sparsity-inducing priors and by the use of expectation maximization algorithms with sparse priors. The focus to date has been on using sparse methods to model continuous data and to carry out sparse feature selection. We describe the relevance vector machine (RVM), a sparse version of the support vector machine (SVM) that is one of the most widely used classification machine learning methods in QSAR and QSPR. We illustrate the superior properties of the RVM by modeling eight data sets using SVM, RVM, and another sparse Bayesian machine learning method, the Bayesian regularized artificial neural network with Laplacian prior (BRANNLP). We show that RVM models are substantially sparser than the SVM models and have similar or superior performance to them.

关键词： Support Vector Network Dataset Machine Learning Bayesian inference Artificial Intelligence machinery vector computer Artificial neural networks machine Quantitative Structure-Activity Relationship Feature extraction classification methods

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An empirical study on classification methods for alarms from a bug-finding static C analyzer

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INFORMATION PROCESSING LETTERS 2007年第2-3期102卷 118-123页

作者： Yi, Kwangkeun Choi, Hosik Kim, Jaehwang Kim, Yongdai Seoul Natl Univ Seoul South Korea

()This work was partially supported by Brain Korea 21 Project of Korea Ministry of Education and Human Resources, by IT Leading R&D Support Project of Korea Ministry of Information and Communication, by Korea Rese... 详细信息

关键词： static analysis abstract interpretation statistical post analysis classification methods program correctness

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N3 and BNN: Two New Similarity Based classification methods in Comparison with Other Classifiers

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2015年第11期55卷 2365-2374页

作者： Todeschini, Roberto Ballabio, Davide Cassotti, Matteo Consonni, Viviana Univ Milano Bicocca Dept Earth & Environm Sci Milano Chemometr & QSAR Res Grp I-20126 Milan Italy

Two novel classification methods, called N3 (N-nearest neighbors) and BNN (binned nearest neighbors), are proposed. Both methods are inspired by the principles of the K-nearest neighbors (KNN) method, being both based on object pairwise similarities. Their performance was evaluated in comparison with nine well-known classification methods. In order to obtain reliable statistics, several comparisons were performed using 32 different literature data sets, which differ for number of objects, variables and classes. Results highlighted that N3 on average behaves as the most efficient classification method with similar performance to support vector machine based on radial basis function kernel (SVM/RBF). The method BNN showed on average higher performance than the classical K-nearest neighbors method.

关键词： Classifiers Nearest neighbor classification methods N3 Stage Finding

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An overview of hyperspectral image feature extraction, classification methods and the methods based on small samples

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APPLIED SPECTROSCOPY REVIEWS 2023年第6期58卷 367-400页

作者： Li, Xueying Li, Zongmin Qiu, Huimin Hou, Guangli Fan, Pingping Qilu Univ Technol Inst Oceanog Instrumentat Shandong Acad Sci Qingdao 266061 Peoples R China China Univ Petr East China Sch Geosci Qingdao Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao Peoples R China

Hyperspectral image (HSI) contains rich spatial and spectral information, which has been widely used in resource exploration, ecological environment monitoring, land cover classification and target recognition. However, the nonlinearity of HSI data and the strong correlation between bands also bring difficulties and challenges to HSI application. In particular, the limited available hyperspectral training samples will lead to the classification accuracy cannot be improved. Therefore, making full use of the advantages of HSI data, through algorithms and strategies to solve the limited training samples, high-dimensional HSI data and effective classification method, so as to improve the classification accuracy. This paper reviews the research results of the feature extraction methods and classification methods of HSI classification in recent years. In addition, this paper expounds five kinds of small sample strategies, and solves the problem of small sample in HSI classification from different angles. Small sample strategy will be the focus of HSI classification research in the future. To solve the problem of small sample classification can greatly promote the application of HSI.

关键词： Hyperspectral image small samples feature extraction classification methods

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An intercomparison of computer-assisted circulation classification methods

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INTERNATIONAL JOURNAL OF CLIMATOLOGY 1996年第8期16卷 893-922页

作者： Huth, R Institute of Atmospheric Physics Prague Czech Republic. email: ivanan@ufa.cas.cz

Five different methods that have been used for classification of circulation patterns (correlation method, sums-of-squares method, average linkage, K-means, and rotated principal component analysis) are examined as to their ability to detect dominant circulation types. The performance of the methods is evaluated according to the degree of meeting the following demands made on the groups formed: The groups should (i) be consistent when preset parameters are changed (ii) be well separated both from each other and from the entire data set, (iii) be stable in space and time, and (iv) reproduce the predefined types. All the methods proved to be capable of yielding meaningful classifications. None of them can be thought of as the best in all aspects. Which method to use will depend mainly on the aim of the classification. Nevertheless, the principal component analysis is most successful in reproducing the predefined types and is therefore considered as the most promising method among those examined.

关键词： circulation patterns classification methods cluster analysis principal component analysis

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