基于卷积神经网络的高光谱遥感地物多分类识别 下载: 1928次
ing 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 multi-classification 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.
闫苗, 赵红东, 李宇海, 张洁, 赵泽通. 基于卷积神经网络的高光谱遥感地物多分类识别[J]. 激光与光电子学进展, 2019, 56(2): 021702. Miao Yan, Hongdong Zhao, Yuhai Li, Jie Zhang, Zetong Zhao. Multi-Classification and Recognition of Hyperspectral Remote Sensing Objects Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(2): 021702.