人工神经网络信息融合及其在机场识别中的应用研究

以机场识别为研究对象,以两种改进的BP网络作为算法工具,以TM影像作为原始数据,在特征级信息融合技术框架下进行机场识别研究。首先对可用于机场识别的特征进行分析,提出使用光谱特征、纹理特征和几何形状特征这三个特征的7维矢量作为BP网络的输入,并对BP网络的训练、测试和识别三个过程进行了详细的讨论。

Feature Level Information Fusion Based on Neural Network and Its Application to Airport Recognition

This paper deals with the airport recognition from optical remote sensing images under the framework of feature level information fusion by using BP neural network. Automatically extracting airport from remote sensing imagery is one of research hots which has being attracted much research interests both from the computer vision community and remote sensing application field. Due to its uniqueness in geometric shape, shape property analysis on airport and recognition based on shape parameters or directly on shape recognition is the intutitive approch in order to recognize airport. However, though straitfordward in method, recognition based on shape analysis only can cause unavoidalbe mistakes when there are objects similar in shape around the airport such as express ways.Thanks to the multispectral property of most commecial remote sensing images, this paper expound an approach for airport recognition based on multi feature fusion, namely, shape parameter, spectral signature and texture feature of an image. Since all these features are different in unit and order of magnitude, it is difficult to combine these features in commonly used algorithms, such as Bayesian inference. Error Back Propagation Neural Network is used to fuse all these features. Two revised version of BP network is used in order of improving the convergence speed during training and recognizing. Real TM images are used for case study, showing the effectiveness of our approach.