基于神经网络的典型地物的偏振多光谱图像分类(特邀)

Polarized multispectral image classification of typical ground objects based on neural network(Invited)

相比传统的多光谱成像探测,偏振多光谱成像探测方法可以探测目标表面的粗糙度、含水量等更多信息,给目标检测带来了很大便利,但目前主要用于目标探测,尚未广泛应用于目标分类。BP神经网络是目前常用的一种典型神经网络,可以建立从端到端的映射,在训练样本集足够大的前提下,训练完毕且效果良好的神经网络是一种高效、精确、快速的工具。首先,利用基于旋转偏振片和滤波片的偏振光谱成像探测系统获取了典型地物的偏振多光谱图像,对图像进行了预处理,建立了数据集;其次,在该数据集上进行了神经网络的训练,训练后的神经网络可以处理未知的偏振多光谱图像,并实现了对几种典型地物的分类;最后,对神经网络分类的效果进行了评价,并与其他几种典型分类方法的效果进行了对比,发现神经网络方法具有更好的分类精度和效果,相比典型的最大似然分类算法,其总体分类精度可从91.7%提升至94.2%,Kappa系数可从0.851提升至0.898。研究结果表明:基于神经网络的偏振光谱图像分类方法对于改进和优化现有的偏振多光谱图像数据处理方法具有一定的研究意义。

Compared with the traditional multispectral imaging detection, polarized multispectral imaging detection can detect more information of the detected object surface such as roughness and moisture content,which brings great convenience to target detection. However, at present, it is mainly used for target detection and not widely used in target classification. BP neural network is a typical neural network commonly used at present.Neural network can establish the start-to-end mapping. On the premise that the training sample set is large enough, the trained neural network with good consequences is an efficient, accurate and high-speed tool. Firstly,the polarized multispectral images of typical ground objects were obtained by using the polarized multispectral imaging detection system based on rotating polarizer and filter, and after the images were preprocessed, the data set could be established;Secondly, the neural network was trained on this data set. The trained neural network could process the unknown polarized spectrum images and realize the classification of several typical ground objects;Finally, the effect of neural network classification was evaluated and compared with several other typical classification methods. It was found that the neural network method has better classification accuracy and effect.Compared with the typical maximum likelihood classification algorithm, its overall classification accuracy could be improved from 91.7% to 94.2%, and the Kappa coefficient could be improved from 0.851 to 0.898. The results show that the polarized multispectral image classification method based on neural network has certain research significance for improving and optimizing the existing data processing methods of polarized multispectral images.