Abstract
Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others having differences in spatial resolution and radiometric features requires many adjustments. To overcome this issue, this paper focused on the DL models trained on datasets that consist of different optical images and a combination of radar and optical data. When dealing with a limited number of training images, the performance of DL models via this approach was satisfactory. They could improve 5-20% of average precision, depending on the optical images tested. Likewise, DL models trained on the combined optical and radar dataset could be applied to both optical and radar images. Our experiments showed that the models trained on an optical dataset could be used for radar images, while those trained on a radar dataset offered very poor scores when applied to optical images.
Abstract (translated)
使用 Deep Learning (DL) 从卫星图像中检测船舶是一种不可或缺的海上监视解决方案。然而,将仅在一个数据集上训练的 DL 模型应用于具有不同空间分辨率和辐射特征的另一个数据集需要进行许多调整。为了克服这个问题,本文重点关注基于不同光学图像和雷达与光学数据组合的数据集的 DL 模型。当处理训练图像数量有限时,通过这种方法训练的 DL 模型的性能是满意的。根据测试的光学图像,它们可以提高 5-20%的平均精度。同样,基于光学和雷达数据集训练的 DL 模型可以应用于光学和雷达图像。我们的实验结果表明,基于光学数据集训练的模型可以用于雷达图像,而基于雷达数据集训练的模型在应用于光学图像时得分非常低。
URL
https://arxiv.org/abs/2403.13698
