基于BP网络的地物影像光谱识别及效果检验

提出了一种基于改进后的BP人工神经网络的地物影像的多波段光谱识别新方法。该方法遵循网络的隐节点数与训练样本数相匹配的网络结构设计的最简原则构建BP网络；采用了随机增加每类样本数，添加样本集中的噪声干扰。从而使噪声起到平滑作用。既防止过度训练，提高了网络的泛化能力。又加快了收敛速度。对11类地物多波段光谱影像实例。通过相继二次构建BP网络模型进行训练，用两次训练好的网络对全部11类地物区分效果明显，达到能完全区分不同地物分类识别的目的。

Spectrum Recognition of Landmark Images Based on BP Network and the Verificagion

A new method for multi-wave band spectrum recognition of landmark images is suggested using improved BP network. The method follows the minimum principle of the structural design of the neural network, in which the training sample numbers match the hidden node numbers. Increasing randomly the numbers of sample of each classification plays a smooth role as a result of the increase of noise interference of sample sets,and it avoids overfitting and enhances the generalization of network, as well as speeds up the learning rates. Through the designing the two grades of BP network, this method was verified by a specific example of landmark images including 11 categories. Results show that all 11 categories of landmarks can be distinguished well using trained BP network,and distinctly the goal of recognition of various categories of landmarks can be achieved.