Adaptive Dual-Path Collaborative Learning for PAN and MS Classification

Due to the limitation of sensor technology, researchers tend to obtain high-quality image information from panchromatic (PAN) images and multispectral (MS) images with different resolutions. Therefore, the classification of remote sensing images of PAN and MS have become a research hotspot. In this paper, we propose an adaptive dual-path collaborative learning method for PAN and MS classification. In the stage of sample generation and training, we propose an adaptive neighborhood sample grading (ANSG) strategy in the establishing sample stage so that each pixel to be classified can obtain neighborhood information suitable for itself. Further, to simulate biological cognitive mechanisms, we divide the samples into different levels, and design the self-paced progressive loss (SPL), thus allowing the network to do preference training in different stages. The network’s training can quickly reach the optimal of the current stage and the overall convergence is more thorough. In the network structure, we propose a dual-path module (DPM) to effectively alleviate the gradient degradation in the residual path , while ensuring maximum gradient loss information flow between every two layers in the densely connected path . This module can extract more robust features to cope with the complex characteristics of remote-sensing images. Moreover, using the characteristics of the dual path to better fuse the features by the gradual collaborative fusion (GCF) way. The experimental results and theoretical analysis have demonstrated the proposed approach’s effectiveness, feasibility, and robustness. Our model are available at https://github.com/AIpy-nan/DBFI-Net.