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Open AccessFeature PaperArticle

A Hybrid Model Integrating Spatial Pattern, Spatial Correlation, and Edge Information for Image Classification

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 South Road, Beijing 100094, China
2
Geospatial Information Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1599; https://doi.org/10.3390/rs11131599
Received: 29 May 2019 / Revised: 29 June 2019 / Accepted: 4 July 2019 / Published: 5 July 2019
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Abstract

This paper develops a novel hybrid model that integrates three spatial contexts into probabilistic classifiers for remote sensing classification. First, spatial pattern is introduced using multiple-point geostatistics (MPGs) to characterize the general distribution and arrangement of land covers. Second, spatial correlation is incorporated using spatial covariance to quantify the dependence between pixels. Third, an edge-preserving filter based on the Sobel mask is introduced to avoid the over-smoothing problem. These three types of contexts are combined with the spectral information from the original image within a higher-order Markov random field (MRF) framework for classification. The developed model is capable of classifying complex and diverse land cover types by allowing effective anisotropic filtering of the image while retaining details near edges. Experiments with three remote sensing images from different sources based on three probabilistic classifiers obtained results that significantly improved classification accuracies when compared with other popular contextual classifiers and most state-of-the-art methods. View Full-Text
Keywords: contextual classification; Markov random fields; spatial covariance; multiple-point statistics contextual classification; Markov random fields; spatial covariance; multiple-point statistics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tang, Y.; Jing, L.; Shi, F.; Li, X.; Qiu, F. A Hybrid Model Integrating Spatial Pattern, Spatial Correlation, and Edge Information for Image Classification. Remote Sens. 2019, 11, 1599.

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