Port ship Detection Based on Visual Saliency model and Center Dark Channel Prior

Abstract

Aiming at the problem that the image texture is complex, the grayscale feature is very close to the wharf, and the accurate segmentation is very difficult in the process of port ship detection in high-resolution optical remote sensing image, a port ship detection method combining visual significance model and center dark channel priori is proposed. The watershed segmentation algorithm based on spectral intensity and image texture information achieves land and sea segmentation. The land area is masked to suppress the interference of complex land background. Extracting fitting line and smoothing port contour line. According to the characteristics of the straight shape of the port and the straight shape of both sides with the ship, the center line point of the line is defined to locate the port, and the set of candidate areas of interest to the port is obtained. Combined with the visual model and the dark channel prior feature, the ship initial significance map and the central dark channel map were fused to complete the ship target detection. Finally, through the bow characteristics of the ship, the adjacent ship is separated to eliminate false alarm and improve the detection accuracy. Experiments show that the algorithm is robust, has a good detection effect, and its accuracy and recall rate are greatly improved.