Abstract
The convolutional neural network (CNN) has been gradually applied to the hyperspectral images (HSIs) classification, but the lack of training samples caused by the difficulty of HSIs sample marking and ignoring of correlation between spatial and spectral information seriously restrict the HSIs classification accuracy. In an attempt to solve these problems, this paper proposes a dual-branch extraction and classification method under limited samples of hyperspectral images based on deep learning (DBECM). At first, a sample augmentation method based on local and global constraints in this model is designed to augment the limited training samples and balance the number of different class samples. Then spatial-spectral features are simultaneously extracted by the dual-branch spatial-spectral feature extraction method, which improves the utilization of HSIs data information. Finally, the extracted spatial-spectral feature fusion and classification are integrated into a unified network. The experimental results of two typical datasets show that the DBECM proposed in this paper has certain competitive advantages in classification accuracy compared with other public HSIs classification methods, especially in the Indian pines dataset. The parameters of the overall accuracy (OA), average accuracy (AA), and Kappa of the method proposed in this paper are at least 4.7%, 5.7%, and 5% higher than the existing methods.
Highlights
The purpose of this section is to introduce a new method model for hyperspectral data processing aimed at classifying hyperspectral images (HSIs)
Compared with several latest HSIs classification methods, two sets are selected as classical data to verify the performance of the proposed deep learning model
In order to verify the classification performance of the proposed dual-branch extraction and classification method (DBECM) method, representative HSI classification methods based on the deep learning strategy, including DBN [50], convolutional neural network (CNN) [49], PF-CNN [51], LSTM [52], 3D-CNN [46], and GLCM-CNN [53] are used for comparison
Summary
The purpose of this section is to introduce a new method model for hyperspectral data processing aimed at classifying hyperspectral images (HSIs). To illustrate the principles and objectives of this work, this section will present hyperspectral data processing, especially the classification tasks, and point out the most widely used classification algorithms. This section will focus on data analysis with deep learning (DL) strategies. A deep learning strategy allows the computer to learn the image features automatically and adds feature learning to the process of model building, reducing the incompleteness caused by artificial design features, especially under complex nonlinear conditions. This section will point out the limitations of these methods when dealing with complex HSIs datasets. To address the limitations of the existing methods, the improved classification method model is proposed in this paper.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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
