Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features
This work addresses monitoring of oil and gas fracking well construction for remote sensing applications, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of monitoring construction activities in low-resolution SAR imagery by detecting vehicles and equipment using texture features, achieving promising classification results with SVM, random forest, and neural network classifiers.
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.