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Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle

To improve the detection precision in complicated backgrounds, a novel rotation-invariant object detection method to detect objects in remote sensing images is proposed in this letter. First, a rotation-invariant feature called radial-gradient angle (RGA) is defined and used to find potential object pixels from the detected image blocks by combining with radial distance. Then, a principal direction voting process is proposed to gather the evidence of objects from potential object pixels. Since the RGA combined with the radial distance is discriminative and the voting process gathers the evidence of objects independently, the interference of the backgrounds is effectively reduced. Experimental results demonstrate that the proposed method outperforms other existing well-known methods (such as the shape context-based method and rotation-invariant part-based model) and achieves higher detection precision for objects with different directions and shapes in complicated background. Moreover, the antinoise performance and parameter influence are also discussed.