Classification is a basic topic in data mining and pattern recognition. Following advances in computer science, a lot of
new methods have been proposed in recent years, such as artificial neural networks, decision trees, fuzzy set and
Bayesian Networks, etc. As a probabilistic network, Bayesian Networks is a powerful tool for handling uncertainty in
data mining and many other domains. Naïve Bayes Classifier (NBC) is a simple and effective classification method,
which is built on the assumption of conditional independence between the class attributes. This topology structure can
not describe the inherent relation among the features. In this paper, we apply Bayesian Networks Augmented Naïve Bayes (BAN) for the texture classification of aerial images, which relaxes the independent assumption in NBC. A new method for learning the networks topology structure based on training samples is adopted in this paper. Comparison experiments show higher accuracy of BAN classifier than NBC. The results also show the potential applicability of the proposed method.
Remote sensing image fusion has become one of hotspots in the researches and applications of Geoinformatics in recent years. It has been widely used to integrate low-resolution multispectral images with high-resolution panchromatic images. In order to obtain good fusion effects, high frequency components of panchromatic images and low frequency components of multispectral images should be identified and combined in a reasonable way. However, it is very difficult due to complex processes of remote sensing image formation. In order to solve this problem, a new remote sensing image fusion method based on frequency domain segmenting is proposed in this paper. Discrete wavelet packet transform is used as the mathematical tool to segment the frequency domain of remote sensing images after analyzing the frequency relationship between high-resolution panchromatic images and low-resolution multispectral images. And several wavelet packet coefficient features are extracted and combined as the fusion decision criteria. Besides visual perception and some statistical parameters, classification accuracy parameters are also used to evaluate the fusion effects in the experiment. And the results show that fused images by the proposed method are not only suitable for human perception but also suitable for some computer applications such as remote sensing image classification.
Networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. Recently Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. However, in the classification domain it was not paid attention to by researchers until the simplest form of Bayesian Networks, Naive Bayesian Network, turned up. In this paper, Naive Bayesian Network is applied to texture classification of aerial image. In order to validate the utility of Naive Bayesian Classifier, six hundred and eighty-four aerial images are used in the experiment and results demonstrate Naive Bayesian Classifier needs less computational costs than maximum likelihood method during classification and outperforms maximum likelihood method in the classification accuracy. Therefore, it is an attractive and effective method, and it will lead to its wide application.
Ant colonies, and more generally social insect societies, are distributed systems that show a highly structured social organization in spite of the simplicity of their individuals. As a result of this swarm intelligence, ant colonies can accomplish complex tasks that far exceed the individual capacities of a single ant. As is well known that aerial image texture classification is a long-term difficult problem, which hasn't been fully solved. This paper presents an ant colony optimization methodology for image texture classification, which assigns N images into K type of clusters as clustering is viewed as a combinatorial optimization problem in the article. The algorithm has been tested on some real images and performance of this algorithm is superior to k-means algorithm. Computational simulations reveal very encouraging results in terms of the quality of solution found.