The rapid, accurate, and automated extraction of surface water is highly important for conducting reliable and necessary surface water monitoring endeavors. Classification methods commonly exhibit high precision but also have a low degree of automation or narrow scope of application; commonly used water index methods are highly efficient, but they easily mistake other targets with similar spectral characteristics for surface water. Simultaneously achieving precision, efficiency, and automation within a single method is a challenge. To address these problems, we simplify the normalized different water index (NDWI) to a band ratio index and traverse the neighborhood of the extreme in the histogram to determine two peaks and one trough between the peaks in the two-mode method, and we then compare the middle value of the two peaks with the value of the trough to confirm the threshold of the surface water. We use the modified two-mode method to extract Poyang Lake from four Chinese Gaofen (GF)-1 remote sensing images corresponding to different seasons, and then compare the results with those obtained by the NDWI index and the maximization of interclass variance (OTSU) method. The comparison shows that our method has higher and more stable accuracy, especially during the drought period for Poyang Lake. However, polluted water, narrow rivers, bridges, and residential areas along the lake are sometimes mistakenly extracted. Finally, the advantages and prospects of the proposed method are discussed.
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.