Objective Limited by flight height and scanning angle, the building façade texture is inevitably obscured resulting in missing unmanned aerial vehicle (UAV) data. However, in the face of a large amount of data, the current inpainting methods that need to manually select the target region appear time-consuming and labor-intensive. Therefore, we propose an automatic inpainting algorithm based on low-rank matrix reconstruction for facade texture, starting from the low-rank property of the building and the sparsity of occlusion, and modeling facade texture repairing as solving a low-rank matrix completion problem.

Methods First, a robust principal component analysis algorithm is utilized to automatically complete the coarse extraction of the sparse occlusion of the façade texture. Second, the sparse region obtained by filling and refining with a priori conditions of the occlusion is used as a constraint for matrix completion. Finally, on the basis of the matrix completion, by adding the truncated Schatten p-norm and the total variation operator to correctly repair the occlusion of the façade.

Results The result shows that our segmentation is as good as the manual marking, inpainting outperforms many state-of-the-art approaches.

Conclusions Our method can automatically and accurately extract the target region, achieving the double improvement of the manpower liberation and the performance.