Laser & Optoelectronics Progress, Volume. 56, Issue 2, 021702(2019)
Multi-Classification and Recognition of Hyperspectral Remote Sensing Objects Based on Convolutional Neural Network
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.
Get Citation
Copy Citation Text
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
Category: Medical Optics and Biotechnology
Received: Jul. 17, 2018
Accepted: Aug. 2, 2018
Published Online: Aug. 1, 2019
The Author Email: Zhao Hongdong (zhaohd@hebut.edu.cn)