Journal ArticleDOI
Fusion of image classifications using Bayesian techniques with Markov random fields
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TLDR
This study investigates whether combining several different image classifications together with an a priori image model of the expected spatial distribution of the classes can produce a better classification.Abstract:
This study investigates whether combining several different image classifications together with an a priori image model of the expected spatial distribution of the classes can produce a better classification. A maximum likelihood classifier and the cascade-correlation neural network architecture are used to generate various classification maps for satellite image data by varying the input features and network parameter settings. A likelihood for each pixel's class label is derived from the source classifications and combined with a Markov random field spatial image model to produce the final image classification. The method is applied to a ground cover type study based on Landsat Thematic Mapper (TM) imagery. It was found that a carefully selected combination could significantly improve individual classification results.read more
Citations
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Journal ArticleDOI
A survey of image classification methods and techniques for improving classification performance
Dengsheng Lu,Qihao Weng +1 more
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Journal ArticleDOI
The application of artificial neural networks to the analysis of remotely sensed data
Jean-François Mas,Juan J. Flores +1 more
TL;DR: An overview of the main concepts underlying ANNs, including the main architectures and learning algorithms, are presented, and the main tasks that involve ANNs in remote sensing are described.
Journal ArticleDOI
Developments in Landsat Land Cover Classification Methods: A Review
Darius Phiri,Justin Morgenroth +1 more
TL;DR: It is suggested that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science, and many advancements in specific classifiers and algorithms have occurred in the last decade.
Journal ArticleDOI
Spatiotemporal Data Mining: A Computational Perspective
Shashi Shekhar,Zhe Jiang,Reem Y. Ali,Emre Eftelioglu,Xun Tang,Venkata M. V. Gunturi,Xun Zhou +6 more
TL;DR: This survey reviews recent computational techniques and tools in spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families, focusing on several major pattern families.
Journal ArticleDOI
Spatial contextual classification and prediction models for mining geospatial data
TL;DR: It is argued that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF, and the relationship between SAR and MRF is analogous to the relationships between regression and Bayesian classifiers.