SARBake Overlays for the MSTAR Dataset

SARBake is an algorithm described in my first article "Convolutional Neural Networks for SAR Image Segmentation" co-authored by Morten Nobel-Jørgensen. The algorithm converts 3D CAD models of objects in to a label mask given the specific details about SAR viewing angles. The mask defines for every pixel whether the radar wave illuminated the object, the background or was in shadow. A good segmentation of a SAR image is very relevant as it simplifies image information. For example can object height above ground be estimated from the length of an objects shadow. An annotated image also enables the possibility of using Supervised Machine Learning techniques to create a segmentation mask automatically. If used in scientific publications we kindly ask for a citation of our article, @article{cnnforsegmentation, author = {David Malmgren-Hansen and Morten Nobel-Jørgensen}, title = {Convolutional Neural Networks for SAR Image Segmentation}, journal = {IEEE International Symposium on Signal Processing and Information Technology}, year = 2015 }

SARBake是一项算法，出自笔者与莫滕·诺贝尔-约根森（Morten Nobel-Jørgensen）合著的首篇学术论文《Convolutional Neural Networks for SAR Image Segmentation》（即《卷积神经网络用于SAR图像分割》）。该算法可依据合成孔径雷达（Synthetic Aperture Radar, SAR）观测角度的具体参数，将物体的三维计算机辅助设计（Computer-Aided Design, CAD）模型转换为标签掩码（label mask）。该掩码会为每个像素标记三类状态：雷达波是否照射到目标、是否属于背景区域，或是处于阴影之中。
SAR图像的高质量分割具备重要的学术与应用价值，因其可有效简化图像信息。例如，可通过物体阴影的长度估算其相对于地面的高度。此外，带标注的SAR图像还可支持使用监督式机器学习技术自动生成分割掩码。
若将本算法应用于学术出版物，敬请引用我们的论文，引用格式如下：
@article{cnnforsegmentation, author = {David Malmgren-Hansen and Morten Nobel-Jørgensen}, title = {卷积神经网络用于SAR图像分割}, journal = {IEEE国际信号处理与信息技术研讨会}, year = 2015 }

搜集汇总

背景与挑战

背景概述

该数据集包含MSTAR数据集中所有图像的SARBake标注和预览，用于SAR图像分割研究，支持监督机器学习技术。数据集关联的出版物探讨了卷积神经网络在SAR图像分割中的应用。