关键词: 高光谱图像分类, 多尺度, 特征融合, 注意力机制

Abstract:

In view of the high dimensional characteristics of hyperspectral images and the limitation of the small number of samples, a multi-scale cross-layer feature fusion network attention mechanism method（MCFFN-Attention） is proposed. PCA reduction is performed on hyperspectral images. Based on 3D CNN, the center pixel and its adjacent pixels are input into the network as a whole, and the features obtained by different convolutional layers are fused. At the same time, spatial attention mechanism processing is carried out for the fused low-level features, and channel attention mechanism processing is carried out for the fused high-level features, and different weights are assigned to them to optimize the feature maps. Experiments on Indian Pines and University of Pavia data sets show that the classification accuracy of this method is improved compared with CNN, 3D CNN and M3D CNN.

Key words: hyperspectral image classification, multi-scale, feature fusion, attention mechanism