MHANet: A Multiscale Hierarchical Pansharpening Method With Adaptive Optimization

Abstract

In recent years, the powerful nonlinear modeling capability of convolutional neural networks has led to an increasing number of researchers focusing on deep learning-based pansharpening methods. However, due to the diversity of remote sensing image features and the limitations of the convolution operation, the existing methods are still inadequate in restoring the spatial details of complex remote sensing scenes. Therefore, in this paper, we propose a simple and effective network for pansharpening methods. Specifically, in our hierarchical feature integration architecture, a multi-scale grouping dilated block is designed to adequately capture fine-grained representations of multi-level scale features. At the same time, we propose a spatially self-attention block to adaptively improve the feature extraction process by establishing associations between features. The above blocks are connected in a hierarchical design, with selective reuse of features between layers, and a good ability to explore new levels of features while reusing low-level features. Our experiments with the GaoFen-2 satellite dataset, WorldView-2 satellite dataset, and WorldView-3 satellite dataset show that our proposed method is highly competitive with existing excellent methods in both objective indicator evaluation and subjective visual evaluation.