基于类别注意力卷积网络的地物分类方法

In recent years, semantic segmentation has made great progress. But most of the methods are from a spatial perspective to obtain richer context information. Different from the previous methods, this paper proposes a feature fusion method based on class attention mechanism, which obtains the global context information from the perspective of category and fuses it with other feature. This method can better represent the features of various objects in the image and has better intra class aggregation. Therefore, this paper uses an ACF (attentional class feature) module to calculate and construct the category centers of all kinds of objects in the image. Based on this, a multi feature fusion semantic segmentation network based on category attention is obtained to achieve better classification performance. The algorithm uses ISPRS data sets for experiments, and compared with other algorithms, the proposed method has better performance.