Classical vs. Machine Learning-Based Inpainting for Enhanced Classification of Remote Sensing Image

Inpainting is a technique that allows for the reconstruction of images and the removal of unnecessary elements. In our research, we employed inpainting to eliminate erroneous lines in the images and examined its abilities in improving classification quality. To reduce the erroneous lines, we designed ResGMCNN, whose multi-column generator model uses residual blocks. For our studies, we used data from the COWC and DOTA datasets. The GMCNN model with residual connections outperformed most classical inpainting methods, including the Telea and Navier–Stokes methods, achieving a maximum structural similarity index measure (SSIM) of 0.93. However, despite the improvement in filling quality, these results still lag behind the Criminisi method, which achieved the highest SSIM values (up to 0.99). We investigated the improvement in classification quality by removing vehicles from the road class in images acquired by UAVs. For vehicle removal, we used Criminisi inpainting, as well as Navier–Stokes and Telea for comparison. Classification was performed using eight classifiers, six of which were based on machine learning, where we proposed our solutions. The results showed that classification quality could be improved by several to over a dozen percent, depending on the metric, image, and classification method. The F1-score and Cohen Kappa metrics indicated an improvement in classification quality of up to 13% in comparison to the classification of the original image. Nevertheless, each of the classical inpainting methods examined improved the road classification.