论文标题
使用图像纹理功能检测SAR图像中车辆和设备的存在
Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features
论文作者
论文摘要
在这项工作中，我们提出了一种监测低分辨率SAR图像中人造的类似建筑式活动的方法。我们的数据来源是欧洲航天局Sentinel-L卫星，该卫星以12天的重新访问率提供全球覆盖范围。尽管解决方案的限制，我们的方法使我们能够通过分析检测到的SAR图像的纹理来监视预定位置的活动水平（即车辆，设备的存在）。使用探索性数据集，我们培训了支持向量机（SVM），随机二进制森林和用于分类的完全连接的神经网络。我们在VV和VH极化通道中使用Haralick纹理功能作为分类器的输入功能。每个分类器都表现出令人鼓舞的结果，能够区分两种可能的施工站点活动水平。本文记录了一项案例研究，该案例研究围绕监视石油和天然气压裂井的建设过程。
In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classification. We use Haralick texture features in the VV and VH polarization channels as the input features to our classifiers. Each classifier showed promising results in being able to distinguish between two possible types of construction-site activity levels. This paper documents a case study that is centered around monitoring the construction process for oil and gas fracking wells.
