基于Mask R-CNN算法的遥感图像处理技术及其应用

Remote Sensing Image Processing Technology and Its Application Based on Mask R-CNN Algorithms

遥感技术的发展使得遥感影像被应用于农业、军事等诸多领域,而深度学习方法的融入使得该项技术在目标检测、场景分类、语义分割方面取得了重大突破。与自然场景下的舰船检测不同,遥感图像中的舰船为俯视图,舰船较为密集,且容易与港口混合。当前对舰船检测的输出结果主要是检测框,缺少对舰船掩码的输出,使得无法全面分析出模型存在的不足;同时,由于遥感图像中的舰船停靠密集,容易产生漏检问题。为解决上述问题,利用Mask R-CNN对舰船进行目标检测,较全面地分析模型的训练情况、掩码和检测框的输出结果;通过对目标边缘的学习及参数的调整,使模型与舰船目标相适应。通过实验分析得出了适用于舰船检测的网络模型参数,从而有效降低了舰船停靠密集所产生的误检和漏检问题。

With the development of remote sensing,there are many fields using remote sensing image,such as agriculture,military and so on.At the same time,deep learning,now,is applying in computer vision and image processing widely.It is successful in object detection,classification and semantic segmentation.Unlike fighting ship detection in natural scenes,fighting ships in remote sensing images are overhead views,dense and easy to mix with ports.The main result on fighting ship is taking bounding box as output,which is lacking the mask of the fighting ship,so may not analyzing the weakness in model.Meanwhile,because of the tight fighting ships in remote sensing images,there are easy to have missed detection.For solving the problems,this paper uses Mask R-CNN to detect fighting ships,analyzing training situation and the results of mask and bounding box.By learning the edges of objects and modifying parameter,making model more suitable to fighting ship.After experiment,it can be concluded that the appropriate parameters can effectively reduce the false positive and false negatives caused by compact berthing of fighting ships.