Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features

TL;DR

This paper introduces a methodology using texture features and machine learning classifiers to detect vehicle and equipment presence in low-resolution SAR imagery for monitoring construction activities.

Contribution

It presents a novel approach combining Haralick texture features with classifiers like SVM, random forest, and neural networks for activity detection in SAR imagery.

Findings

Classifiers successfully distinguish activity levels in SAR images.

Haralick features effectively capture texture information for classification.

Method demonstrates potential for real-time monitoring of construction sites.

Abstract

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.