Terrain Classification Of Aerial Image Based On Low-Rank Recovery And Sparse Representation
2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION)(2017)
摘要
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.
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关键词
terrain classification, airborne vision sensor, unmanned aerial vehicle, low rank recovery, sparse representation
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