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The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification...
This paper proposed a new strategy for spectral-spatial hyperspectral image classification. The proposed strategy, has concentrated on spatial graph kernel and automatic “outstanding” spatial structures. Contribution of this paper is related to analysing probabilistic classification results for selecting the most reliable classified pixels as outstanding points of spatial regions. Experimental implementations...
This paper introduces a classification system for remote sensing ASTER satellite imagery using SVM and particle swarm optimization (PSO) algorithm. The proposed system starts with the identification of selected area of study. This is followed by a pre-processing phase using mapping polynomial algorithm as geometric correction. Followed by, applying threshold algorithm for image segmentation. Then...
High spatial resolution images are widely used in many different areas. In order to increase classification accuracy, texture feature are widely studied. This paper presents a method of object oriented texture analysis method based on Gabor filters. In fusion of spectral features and Gabor textures, composite kernel methods were applied. The results show its effectiveness in extracting texture information...
Remote sensing image segmentation requires multi-category classification typically with limited number of labeled training samples. While semi-supervised learning (SSL) has emerged as a sub-field of machine learning to tackle the scarcity of labeled samples, most SSL algorithms to date have had trade-offs in terms of scalability and/or applicability to multi-categorical data. In this paper, we evaluate...
Logistic Regression has become a commonly used classifier, not only due to its probabilistic output and its direct usage in multi-class cases. We use a sparse Kernel Logistic Regression approach - the Import Vector Machines - for land cover classification. We improve our segmentation results applying a Discriminative Random Field framework on the probabilistic classification output. We consider the...
Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison,...
Support vector machines (SVMs) is a statistical learning method with good performance when the sample size is small, due to their excellent performance, SVMs are now used extensively in pattern classification applications and regression estimation, Unfortunately, it is currently considerably slower in test phase caused by number of the support vectors, which has been a serious limitation for some...
Since inadequacy information from spectral characteristics for very high resolution remote sensing multispectral imagery segmentation/classification, we propose the combination of spectral feature which was extracted by a variable mean shift clustering algorithm and spatial features by Gabor filter banks and support vector machine is employed to achieve feature fusion and classification. Some issues...
As to high-resolution remote sensing imagery classification based on object-orientated methodology, the precision is related to feature configuration and classification algorithm. In this paper, the IKONOS multi-spectral image is selected as sample data, and segmentated into many image objects. Firstly, the supervising classification is applied to extract land cover thematic information, based on...
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