基于卷积神经网络的高光谱遥感地物多分类识别

Multi-Classification and Recognition of Hyperspectral Remote Sensing Objects Based on Convolutional Neural Network

在进行遥感图像多分类识别时,针对使用传统方法遇到的分类模型特征提取困难、分类精度不理想、分类种类少等问题,研究了卷积神经网络(CNN)模型在高光谱遥感地物多分类识别中的可行性及不同CNN模型对高光谱遥感地物多分类的识别效果。从ISPRS(International Society for Photogrammetry and Remote Sensing)提供的Vaihingen及Google Earth中采集数据,制作了包含6类地物的数据集一。在此基础上增加10类地物制作数据集二,再增14类地物制作数据集三。在预处理图像数据之后,通过设置神经网络结构、调整模型参数、对比神经网络模型等,上述3类数据集的地物分类识别率均达到95%以上。通过分析不同CNN模型对高光谱遥感地物多分类识别效果的影响,证实了CNN模型在高光谱遥感地物多分类识别应用的可行性且具有较高的识别率。实验结果为CNN模型在高光谱遥感地物多分类识别中的应用提供了一定的参考。

Aiming at the problems of difficult feature extraction,poor classification accuracy,and less classification types in the remote sensing image multi-classification by the conventional methods,the feasibility of the convolutional neural network(CNN)model and the recognition effects of different CNN models are studied in the multi-classification recognition of hyperspectral remote sensing objects.The datasets are collected from Vaihingen provided by the international society for photogrammetry and remote sensing(ISPRS)and Google Earth.After the dataset-I containing six categories of ground objects is made,the dataset-II and dataset-III are made by adding ten and fourteen categories of ground objects,respectively.Through pre-processing image data,setting up network structures,adjusting model parameters,comparing network models,and so on,the classification accuracies of the above three datasets are all above 95%.By analyzing the influences of different CNN models on the multiclassification recognition of hyperspectral remote sensing objects,the feasibility and high recognition ability of CNN model in the multi-classification recognition of hyperspectral remote sensing are confirmed.The experimental results provide a certain reference for the application of CNN model in the multi-classification recognition of hyperspectral remote sensing objects.