SVM
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Support Vector Machine (SVM) Classifier
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Download SVM Tutorial

Download SVM Tutorial | SVM | Scoop.it
“Introduction to Support Vector Machine (SVM). Tutorial on SVM for Data Mining, Pattern Recognition, and Machine Learning using spreadsheet without programming”
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Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data

Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data | SVM | Scoop.it

The classification of remote sensing data with imbalanced training data is addressed. The classification accuracy of a supervised method is affected by several factors, such as the classifier algorithm, the input data and the available training data. The use of an imbalanced training set, i.e., the number of training samples from one class is much smaller than from other classes, often results in low classification accuracies for the small classes. In the present study support vector machines (SVM) are trained with imbalanced training data. To handle the imbalanced training data, the training data are resampled (i.e., bagging) and a multiple classifier system, with SVM as base classifier, is generated. In addition to the classifier ensemble a single SVM is applied to the data, using the original balanced and the imbalanced training data sets. The results underline that the SVM classification is affected by imbalanced data sets, resulting in dominant lower classification accuracies for classes with fewer training data. Moreover the detailed accuracy assessment demonstrates that the proposed approach significantly improves the class accuracies achieved by a single SVM, which is trained on the whole imbalanced training data set.

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Geomatics Department - Image SVM Software

Geomatics Department - Image SVM Software | SVM | Scoop.it

ImageSVM is an IDL based tool for the support vector machine classification and regression analysis of remote sensing image data. Its workflow allows a flexible and transparent use of the support vector machine (SVM) concept for both simple and advanced classification/regression approaches.

imageSVM advances the use of the SVM in the field of remote sensing image analysis by

- offering a platform and license independent implementation for SVM classification and regression,

- enabling the use of common image file formats for data in- and output,

- integrating a widely accepted, powerful algorithm for the training of the SVM that is open-source and updated by machine learning specialist on a regular basis,

- offering alternative workflows for automized parameterization (by default values) and user-defined parameterization (limited to parameters relevant for remote sensing),

- visualizing training parameters and intermediate results in a transparent workflow to increase the understanding and acceptance of the support vector approach in the remote sensing.

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Classification of Hyperspectral Remote Sensing Images With Support Vector Machines

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Evaluation of the RapidEye red edge channel for improving land-use classifications

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IMPROVEMENT OF LAND COVER CLASSIFICATION PERFORMANCE IN WESTERN AUSTRALIA USING MULTISOURCE REMOTE SENSING DATA

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DETECTION AND ANALYSIS OF DEFORESTATION IN CLOUD-CONTAMINATED LANDSAT IMAGES: A CASE OF TWO PHILIPPINE PROVINCES WITH HISTORY OF FOREST RESOURCE UTILIZATION

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Comparison of Advanced Pixel Based (ANN and SVM) and Object-Oriented Classification Approaches Using Landsat-7 Etm+ Data

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MODEL-BASED ESTIMATION OF IMPERVIOUS SURFACE BY APPLICATION OF SUPPORT VECTOR MACHINES

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Support Vector Selection and Adaptation for Classification of Remote Sensing Images

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EVALUATION OF FACTORS AFFECTING SUPPORT VECTOR MACHINES FOR HYPERSPECTRAL CLASSIFICATION

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Using lidar remote sensing and support vector machines to classify fire disturbance legacies in a Florida oak scrub landscape

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A COMPARATIVE STUDY OF LANDSAT 5 TM LANDUSE CLASSIFICATION NETWORK AND SUPPORT VECTOR MACHINE FOR USE IN A SIMPLE HYDROLOGIC BUDGET MODEL

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IEEE Xplore - Fusion of ALOS Palsar and Landsat ETM data for land cover classification and biomass modeling using non-linear methods

This work demonstrates the utility of reduced resolution ALOS PALSAR data for biomass mapping and land cover classification over the tropical forests of Indonesia. This study is important because we processed the ALOS PALSAR mosaic, which is made freely available within K&C initiatives project and will be updated regularly. We first used 38 sample plots collected on the ground during dry season in September 2004, to develop a tree diameter (dbh)-biomass model. The HH, HV, HV/HH and HH-HV backscatters of ALOS PALSAR data allowed the empirical estimation of forest above ground biomass (AGB). Each band of PALSAR data was separately used to estimate the biomass, and we found HV band resulted in better correlation with the AGB compared to other SAR bands. Validation of the prediction results was carried out by comparing the biomass estimates with those predicted from an existing allometric equation. Optical data are sensitive to the physical properties of the reflectors whereas SAR data are more influenced by the geometric properties of the scatterers. Therefore, the second part of this study concerned the integration of mosaic SAR textures and ETM data for land cover classification. The classification was conducted using ETM data and variations of ETM, SAR bands, and SAR textures calculated using GLC Matrix. The image classifications were carried out using a Machine Learning based classifier, so-called Support Vector Machine (SVM), and a conventional Maximum Likelihood method. An ensemble of neural networks method using Kalman filter and scaled conjugate gradient algorithm was applied. The classification accuracy was assessed using confusion matrices and Kappa statistics. We show that the introduction of SAR textures significantly enhanced the classification accuracies. This study showed that the joint processing of SAR and multispectral data increased the accuracies of biomass estimation and landuse classifications. The efficiency of the method at medium spatial r- esolutions allows its application of global datasets.

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Land cover mapping of large areas using support vector machines for a chain classification of neighboring Landsat satellite images.

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