Terrain classification of aerial image based on low-rank recovery and sparse representation

It is critical to classify the landing terrain from aerial images when an unmanned aerial vehicle lands at an unprepared site autonomously by using a vision sensor. Owing to the interference of illumination variations and noises, different terrains may show a similar image feature and the same terrain may have a different image feature, which brings great difficulties to image classification. To address this issue, a terrain classification method based on low-rank recovery and sparse representation is proposed. Color moments and Gabor texture feature are extracted and fused to construct a discriminative dictionary. Then, we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and classify the test samples by sparse-representation-based classification. Experimental results on an aerial image database that we prepared by using the DJI Phantom 3 Advanced UAV verify the classification accuracy and robustness of the proposed method.