Although deep learning-based pansharpening methods for remote sensing images have made certain progress， most of them rely on supervised training with downsampled data， making them susceptible to scale bias and difficult to maintain stable performance at full resolution. In contrast， unsupervised methods optimize directly on full-resolution images， avoiding issues caused by downsampling， but generally exhibit weak robustness due to the lack of explicit supervisory signals. Therefore， a pansharpening Network based on Two-stage Collaborative Optimization （TCONet） was proposed. In the first stage， through supervised training on downsampled data， and combining an Improved Multi-Resolution Analysis （IMRA） method with an attention mechanism， spatial details and spectral preservation capability were improved. In the second stage， an Unsupervised Information Compensation Network （UCIN） was constructed to directly optimize on full-resolution images， thereby compensating for information loss caused by scale inconsistency. Experimental results on three satellite datasets： QuickBird（QB）， WorldView-2 （WV-2）， and WorldView-4 （WV-4） indicate that TCONet outperforms comparative methods in terms of both visual quality and evaluation metrics.