目的 星上的舰船检测需要在资源和时间受限条件下实现快速检测，并且对目标的种类和尺寸缺少先验信息的指导，更多时候还需要实现一景图像中不同尺寸舰船的检测，因此，星上舰船检测要求检测方法具有一定的自适应性，从而实现星上多变的检测场景。方法 针对这一问题，提出了一种多尺度分形维的检测方法，可以实现一景遥感图像中不同尺寸舰船目标的检测。首先，针对差分盒算法受盒子尺寸约束的限制使分形维数的计算精度受到影响的问题提出了一种改进算法，改进算法增加了拟合直线的点对数目并引入了拟合误差剔除误差点对，提高了分形维特征计算的精确度。结果 在提高了分形维计算精度的基础上，新算法利用自然物体在不同尺度上具有的自相似性，通过多尺度分形维的计算并借鉴视觉显著性中c-s算子来排除背景对目标的干扰，突出舰船目标。实验结果表明，新算法能够有效检测出一景图像中不同尺寸的舰船，优于双参数CFAR算法的检测结果。结论 本文提出的多尺度分形维的检测算法可以实现对一景图像中不同尺寸舰船目标的检测，在保证一定检测率的同时有效降低了目标检测的虚警率。

Objective Detection of ship targets in seas is an important research field in remote sensing image target detection.Onboard ship detectors need to detect targets rapidly under limited resources and time-constraint without the prior information about the type and size of the ship targets as guidance.In detection of vessel formation,different sizes of ships in a scene are normally present.Thus,an onboard ship detection method needs adapt to changeable detection scene.Method To solve this problem,a novel multi-scale fractal dimension based onboard ship saliency detection algorithm is proposed,which can detect ship targets of different sizes.Analysis of natural texture images showed that the images of the natural scenes fit the fractal Brown random field.The study of images,such as sea waves,clouds,and other natural objects,shows that the natural objects all have fractal characteristics,whereas ships,aircrafts,vehicles,and other man-made objects hardly show any fractal characteristics.Therefore,the fractal difference between natural background and man-made targets can be used for target detection and recognition of the targets.The key problem is how to estimate the fractal dimension accurately.An improved algorithm is proposed considering that the fractal dimension calculation accuracy is affected by the constrained size of boxes of the differential box counting(DBC) algorithm.In the improved algorithm,the numbers of pairs of points of the fitting line are increased,and the fitting error to eliminate the error pairs of points is introduced.Thus,the calculation accuracy of the fractal dimension features is improved.Experimental results show that the improved algorithm is more accurate in the fractal dimension calculation of small images than the classic DBC algorithm.Based on the accuracy improved fractal dimension,a center-surround(c-s) operator based on the detection principle of Itti model is used in the new algorithm to eliminate the natural background and highlight the ship targets simultaneously given that the natural objects show the self-similarity at different scales,which is different from the man-made objects.The two-parameter CFAR detection method is a classical algorithm for ship target detection commonly used in optical remote sensing images.This method is suitable for the detection of complex image objects with local background changes.Result The proposed algorithm is compared to the two parameter CFAR algorithm.For the different sizes of ship target detection in one scene,the more obvious ship targets are highlighted by the two-parameter CFAR algorithm,and the targets that are considerably different from the obvious ones in size are easily missed.Thus,the detection rate of target detection is reduced.Moreover,ship targets of different sizes are highlighted through the multi-scale approach of the new algorithm and the background is weakened,which is conducive for ship target detection with different sizes.In the experiment,25 remote sensing images are selected.The total number of ship targets in the selected images is 102,and the size of the ship targets varies in a scene,in which the two algorithms are used to detect the ship targets.Compared with the two-parameter CFAR algorithm,the ship detection method based on the multi-scale fractal dimension has a higher detection rate and lower false alarm rate.Conclusion The proposed detection algorithm based on the multi-scale fractal dimension can realize the detection of differently-sized ship targets in a scene of remote sensing images,thus effectively reducing the false alarm rate of target detection while ensuring a certain detection rate.To cope with the changeable and complex detection scenarios of onboard imaging,the new algorithm,which can adapt to different sizes of ship target detection in one scene,is more flexible and has strong adaptability.