Abstract
In order to effectively extract features and improve classification accuracy for hyperspectral remote sensing images (HRSIs), the advantages of enhanced particle swarm optimization (PSO) algorithm, convolutional neural network (CNN), and extreme learning machine (ELM) are fully utilized to propose an innovative classification method of HRSIs (IPCEHRIC) in this paper. In the IPCEHRIC, an enhanced PSO algorithm (CWLPSO) is developed by improving learning factor and inertia weight to improve the global optimization performance, which is employed to optimize the parameters of the CNN in order to construct an optimized CNN model for effectively extracting the deep features of HRSIs. Then, a feature matrix is constructed and the ELM with strong generalization ability and fast learning ability is employed to realize the accurate classification of HRSIs. Pavia University data and actual HRSIs after Jiuzhaigou M7.0 earthquake are applied to test and prove the effectiveness of the IPCEHRIC. The experiment results show that the optimized CNN can effectively extract the deep features from HRSIs, and the IPCEHRIC can accurately classify the HRSIs after Jiuzhaigou M7.0 earthquake to obtain the villages, bareland, grassland, trees, water, and rocks. Therefore, the IPCEHRIC takes on stronger generalization, faster learning ability, and higher classification accuracy.
Highlights
Remote sensing image (RSI) classification is to divide the image into several regions by using specific rule or algorithm according to the spectral features, geometric texture features, or other features [1–3]
To prove the ability of the IPCEHRIC to solve practical engineering problems, the hyperspectral remote sensing images after Jiuzhaigou M7.0 earthquake is used for the experimental comparison and analysis
An innovative hyperspectral remote sensing image classification method based on combining CWLPSO, convolutional neural network (CNN), and extreme learning machine (ELM), namely IPCEHRIC is proposed to obtain the accurate classification results
Summary
Remote sensing image (RSI) classification is to divide the image into several regions by using specific rule or algorithm according to the spectral features, geometric texture features, or other features [1–3]. Hilal et al [59] presented a new deep transfer learning-based fusion model for remote-sensing image classification. Because the CNN has good feature extraction ability, these classification methods based on CNN have obtained better classification effects It has attracted extensive attention and has been widely applied in RSIs. the structure and parameter selection of the CNN seriously affect its learning accuracy. For the difficultly determining the parameters of the CNN, an optimized CNN model using CWLPSO is developed to effectively extract the deep features of HRSIs. The ELM with strong generalization ability, fast learning ability, and the constructed feature vector are combined to realize the accurate classification of HRSIs. An innovative classification method of HRSIs based on CWLPSO, CNN, and ELM, namely, IPCEHRIC is proposed
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call