Low-Rank Matrix Aided Automatic Texture Inpainting of Building Facades from UAV Images

Objective: Limited by flight height and scanning angle, the building facade 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 needs to manually select the target region appears time-consuming and labor-intensive. Therefore, this paper proposes 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 facade texture; Second, the sparse region obtained by filling and refining with the 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 pNorm and the total variation operator to correctly repair the occlusion of the facade. Result: The result show 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.