No abstract

No abstract

Classification methods based on Gaussian Markov Measure Field Models and other probabilistic approaches have to face the problem of construction of the likelihood. Typically, in these methods, the likelihood is computed from 1D or 3D histograms. However, when the number of information sources grows, as in the case of satellite images, the histogram construction becomes more difficult due to the high dimensionality of the feature space. In this work, we propose a generalization of Gaussian Markov Measure Field Models and provide a probabilistic segmentation scheme, which fuses multiple information sources for image segmentation. In particular, we apply the general model to classify types of crops in satellite images. The proposed method allows us to combine several feature spaces. For this purpose, the method requires prior information for building a 3D histogram for each considered feature space. Based on previous histograms, we can compute the likelihood of each site of the image to belong to a class. The computed likelihoods are the main input of the proposed algorithm and are combined in the proposed model using a contrast criteria. Different feature spaces are analyzed, among them are 6 spectral bands from LANDSAT 5 TM, 3 principal components from PCA on 6 spectral bands and 3 principal components from PCA applied on 10 vegetation indices. The proposed algorithm was applied to a real image and obtained excellent results in comparison to different classification algorithms used in crop classification.

El trabajo se desarrolló durante el periodo de Julio-noviembre del año 2015, al interior del Centro Universitario de los Valles de la Universidad de Guadalajara (CUValles) con estudiantes de la propia dependencia, participando en su estructura e indagatoria el Mtro. Sergio Eduardo Arévalo Contreras y el Mtro. Francisco E. Oliva, así como un grupo de apoyo de estudiantes de la carrera de psicología del 4° semestre de la asignatura reporte de investigación de campo.