Forest classification based on GF-5 hyperspectral remote sensing data in Northeast China

Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology and spectral technology. It can obtain continuous and narrow band image data with high spectral resolution. Therefore, hyperspectral remote sensing has great potential in the identification of ground features and the classification of vegetation types. In this paper, GF-5 data was used as training data to classify forest types in Northeast China. Firstly, the water absorption bands and some noise bands were removed from the GF-5 hyperspectral image. Furthermore, the bands were grouped according to their correlation, and principal component analysis (PCA) was performed on each group of bands. According to the band index, the bands with better quality were extracted from each group and combined with the bands obtained by PCA to reduce the dimension of hyperspectral data. Then the Convolutional Neural Network (CNN) was used to extract the features of the processed image, and the extracted features were input into the support vector machine (SVM) classifier to obtain the forest vegetation type. By combining CNN and SVM, a hyperspectral forest classification model based on CNN-SVM fusion is constructed. The experimental results show that the method proposed in this paper performs best in forest type classification accuracy. The overall classification accuracy can reach 88.67%, and the Kappa coefficient can reach 0.84.