Different types of clouds have different radiative effects on thenthereby the Earth surface-atmosphere system. Accurate and automatic cloud detection and Inhibitors,Modulators,Libraries classification are useful for numerous climatic, hydrologic and atmospheric applications [4]. Therefore, an accurate and cost-effective method of cloud detection and classification based on satellite images has been a great interest of many scientists [5,6].Cloud classification methods can mainly be divided into following categories: the threshold approach, traditional statistical methods and new methods such as Artificial Neural Network (ANN). The threshold methods were mainly developed during the 1980s and early 1990s. They apply a set of thresholds (both static and dynamic) of reflectance, brightness temperature and brightness temperature difference [7,8].
They are the simplest and probably most commonly used methods. However, these methods may fail when two different classes have no obvious brightness temperature difference Inhibitors,Modulators,Libraries (i.e., indistinct threshold) because of the complexity of the cloud system. The traditional statistical methods, such Inhibitors,Modulators,Libraries as clustering method, histogram approach and others [9�C11] are supposed to be superior to the threshold methods to conduct cloud classification and detection in that they digest more information by using all the available bands but they can hardly separate clusters with significant overlapping spaces.With a rapid development in technological innovations, at present, some new methods, such as neural network [12], Bayesian methods [13], maximum likelihood [14] and fuzzy logic [6], have provided impressive results for cloud detection and classification.
Many studies have acknowledged that the well-trained cloud classification neural networks usually have relatively superior performance [15,16]. In fact, almost all the classification Inhibitors,Modulators,Libraries methods in the first two categories can be seen as a special or simpler case of neural networks [17]. Therefore, since the first application of ANN in cloud classification [12], lots of ANN methods Carfilzomib have been applied to satellite infrared images. For example MLP (Multilayer Perceptron) on LandSAT [18] and on NOAA-AVHRR [19], PNN (Probabilistic Neural normally Network) on GOES-8 and AVHRR [20,21], the combination of PNN and SOM (Self-Organizing Map) on Meteosat-7 [22], RBF (Radial Basis Functions) on GMS-5 [23,24] and so on. However, there is inadequate study to evaluate the performance and capacity of these ANN classifiers on multi-channels satellite imagery. Historically, due to diversity of cloud dynamics and complexity of underlying surface, it is not uncommon to find out that single Infrared channel data could not effectively identify cloud types because different cloud types might have similar cloud-top brightness temperatures (Tbb).