Wang L C, Der S Z, Nasrabadi N M
SONY Semicond. Co. if America, San Jose, CA 95134, USA.
IEEE Trans Image Process. 1998;7(8):1113-21. doi: 10.1109/83.704305.
A modular neural network classifier has been applied to the problem of automatic target recognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multiresolution features and by the introduction of a higher level neural network on the top of the individual networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data-decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of real FLIR images.
一种模块化神经网络分类器已被应用于使用前视红外（FLIR）图像的自动目标识别问题。该分类器由几个独立训练的神经网络组成。每个神经网络基于从目标图像的特定部分提取的局部特征做出决策。各个网络的分类决策被组合起来以确定最终分类。实验表明，在网络复杂度和分类概率方面，输入特征的分解导致性能优于全连接网络。通过使用多分辨率特征以及在各个网络之上引入更高层次的神经网络（一种称为堆叠泛化的方法），分类器的性能进一步提高。除了特征分解，我们还实现了一个数据分解分类器网络并展示了改进的性能。在大量真实FLIR图像上报告了实验结果。
