Multiple Spatial Features Extraction and Fusion for Hyperspectral Images Classification

In recent decades, spatial feature extraction has greatly improved the performance of hyperspectral image (HSI) classification. This paper presents an HSI classification method based on multiple spatial features extraction and fusion (MSFs-EF). The method consists of five sequential steps. 1- Principal component analysis is applied for HSI dimensionality reduction. 2- A mean curvature filter is used to extract the spatial texture features from the HSI. 3- The spatial correlation features are obtained using a domain transform normalized convolution filter. 4- Spatial texture features and spatial correlation features are combined. 5- The multiple spatial features are classified using the Large Margin Distribution Machine. Three hyperspectral data sets are used to verify the performance. This method improves the accuracy of HSI classification compared with SVM method, edge-preserving filter, recursive filter method, and deep learning method. In the case of ratios of training samples of 5%, 0.6%, and 5%, the overall accuracy of three data sets reaches 98.23%, 99.17%, and 98.21% respectively, and are about 1.3%similar to 19%, 0.2%similar to 13%, and 0.4%similar to 13% higher than other fourteen algorithms. In the case of ratios of training samples of 10%, 1%, and 10%, the overall accuracy of the three data sets reaches 98.63%, 99.53%, and 98.99%, respectively and still outperform other methods.