Hyperspectral image classification based on octave convolution and multi-scale feature fusion

Hyperspectral Image (HSI) classification is one of the main research directions of remote sensing applications. With the high dimension, strong correlation, and a large amount of HSI data, conventional classification methods often have problems like computation complexity, low classification accuracy, and poor generalization ability while extracting remote sensing image features. Moreover, the extraction of efficient features will impact the classification results. This work proposed an HSI classification method based on octave convolution and multi scale feature fusion. The low-level features of HSI were extracted by octave convolution, and the attention mechanism was introduced into the spatial and spectral dimensions to focus on the area of interest. Then, the spectral-spatial fusion features were extracted for the classification task. Based on 300 training samples, several experiments were carried out, and the results showed that the classification accuracy of the proposed method was 99.63%, 97.90%, and 98.69% on the widely used WHU-Hi dataset. The notable observation was that fusion of features of different scales helped improve the classification performance.

Automated summary

This paper proposes an HSI classification method based on octave convolution and multi-scale feature fusion. By introducing attention mechanism and extracting spectral-spatial fusion features, the method achieves high classification accuracy on the WHU-Hi dataset.