Research on classification method of hyperspectral remote sensing images based on multiscale multichannel CNN

Convolutional neural network (CNN) is recognized and widely applied to classify hyperspectral remote sensing images (HSI). For the existing hyperspectral image classification algorithms, the network structure is simple, the generalization performance of the feature extraction model is poor, and the training samples are not uniform, this paper proposes a multi-scale multi-channel convolutional neural network (MMC-CNN) model based on the feature fusion. Firstly, the data is divided into two scales 𝑿𝟏 × 𝑿𝟏 pixel module and 𝑿𝟐 × 𝑿𝟐 pixel module, and then the two module channels are used again for feature extraction respectively, and three-dimensional convolution is used as the feature extractor in each channel. The experimental results show that the present model has a significant improvement in classification accuracy on commonly used HSI datasets, which is better than previous algorithms, thus verifying the high accuracy of the feature extraction of the present model.