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Sensors 2017, 17(6), 1373;

Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources

Centro de Investigación en Matemat́icas, CIMAT, 36023 Guanajuato, Mexico
Centro Universitario de los Valles, Universidad de Guadalajara, 46600 Ameca, Jalisco, Mexico
This paper is an extended version of our paper published in Oliva, F.E.; Dalmau, O.S.; Alarcón, T.E.  Classification of Different Vegetation Types Combining Two Information Sources Through a Probabilistic  Segmentation Approach. In Proceedings of the 14th Mexican International Conference on Artificial Intelligence  (MICAI), Morelos, Mexico, 25–31 October 2015.
These authors contributed equally to this work.
Authors to whom correspondence should be addressed.
Received: 11 May 2017 / Revised: 7 June 2017 / Accepted: 8 June 2017 / Published: 13 June 2017
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
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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. View Full-Text
Keywords: probabilistic segmentation; remote sensing; likelihood; vegetation indices; histogram probabilistic segmentation; remote sensing; likelihood; vegetation indices; histogram

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Dalmau, O.S.; Alarcón, T.E.; Oliva, F.E. Crop Classification in Satellite Images through Probabilistic Segmentation Based on Multiple Sources. Sensors 2017, 17, 1373.

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