Inshore ship detection from high-resolution satellite images is a useful yet challenging task in remote surveillance and military reconnaissance. It is difficult to detect the inshore ships with high precision because various interferences are present in the harbor scene. An inshore ship detection method based on the weighted voting and rotation-scale-invariant pose is proposed to improve the detection performance. The proposed method defines the rotation angle pose and the scaling factor of the detected ship to detect the ship with different directions and different sizes. For each pixel on the ship template, the possible poses of a detection window are estimated according to all possible pose-related pixels. To improve robustness to the shape-similar distractor and various interferences, the score of the detection window is obtained by designing a pose weighted voting method. Moreover, the values of some parameters such as similarity threshold and the weight of “V” are investigated. The experimental results on actual satellite images demonstrate that the proposed method is invariant to rotation and scale and robust in the inshore ship detection. In addition, better detection performance is observed in comparison with the existing inshore ship detection algorithms in terms of precision rate and recall rate. The target pose of the detected ship can also be obtained as a byproduct of the ship detection.

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.

This paper proposes a color-direction patch sparsity-based image in painting method to better maintain structure coherence, texture clarity, and neighborhood consistence of the in painted region of an image. The method uses super-wavelet transform to estimate the multi-direction features of a degraded image, and combines with color information to construct the weighted color-direction distance (WCDD) to measure the difference between two patches. Based on the WCDD, the color-direction structure sparsity is defined to obtain a more robust filling order and more suitable multiple candidate patches are searched. Then, the target patches are sparsely represented by the multiple candidate patches under neighborhood consistency constraints in both the color and the multi-direction spaces. Experimental results are presented to demonstrate the effectiveness of the proposed approach on tasks such as scratch removal, text removal, block removal, and object removal. The effects of super-wavelet transforms and direction features are also investigated.