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In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm...
This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework,...
This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with...
With development of hyperspectral imaging, it is possible to identify and classify land cover with more details in remote sensing applications. Selection of a minimal and efficient subset from the huge amount of features is an important challenge for classification problems. Almost all approaches for feature selection, which represented in literature, involve a search algorithm for selection of the...
In this paper, a novel semisupervised algorithm is proposed with spatial similarity for classification of hyperspectral data based on the transductive support vector machines algorithm. The proposed method exploits the characteristic of the hyperspectral data, in which spatially nearby points are likely to belong to the same class. To utilize this assumption, a novel transductive support vector machines...
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