Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields
نویسندگان
چکیده
منابع مشابه
Bayesian image classification using Markov random fields
In this paper, we present three optimisation techniques, Deterministic Pseudo-Annealing (DPA), Game Strategy Approach (GSA), and Modified Metropolis Dynamics (MMD), in order to carry out image classification using a Markov random field model. For the first approach (DPA), the a posteriori probability of a tentative labelling is generalised to a continuous labelling. The merit function thus defi...
متن کاملPET image segmentation using a Gaussian mixture model and Markov random fields
BACKGROUND Classification algorithms for positron emission tomography (PET) images support computational treatment planning in radiotherapy. Common clinical practice is based on manual delineation and fixed or iterative threshold methods, the latter of which requires regression curves dependent on many parameters. METHODS An improved statistical approach using a Gaussian mixture model (GMM) i...
متن کاملSpectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields
This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial infor...
متن کاملClassification of textures using Gaussian Markov random fields
The problem of texture classification arises in several disciplines such as remote sensing, computer vision, and image analysis. In this paper we present two feature extraction methods for the classification of textures using two-dimensional (2-D) Markov random field (MRF) models. It is assumed that the given M x M texture is generated by a Gaussian MRF model. In the first method, the least squ...
متن کاملUnmixing hyperspectral images using Markov random fields
This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Geoscience and Remote Sensing Letters
سال: 2014
ISSN: 1545-598X,1558-0571
DOI: 10.1109/lgrs.2013.2250905