Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis

Abstract

Abstract:In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies' contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads-rectangular features of bare land covering an area of approximately 50-60 m × 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR) and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery) and the possibility of detailed land use classification (vs. single-pol SAR). The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user's accuracy of the methodology by an order of a magnitude. The final achieved user's and OPEN ACCESSRemote Sens. 2014, 6 11978 producer's accuracy is 59%-71% in each case (area based accuracy assessment). Considering only the numbers of correctly/falsely detected oil well pads, the user's and producer's accuracies increase to even 74%-89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are determined. The high transferability of the methodology is proved by an application to a second SAR acquisition.

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

“…For the extraction of linear features, the method was well-suited to identify dirt roads and infrastructure from PlanetScope and RapidEye imagery, which obtained accurate results and opened up possibilities to apply the methodology to other areas. Consequently, the classification model achieved affordable results with high accuracy by the basis of different satellite data, which is consistent with the findings of other studies [22][23][24]52]. However, as could be seen in the performance of the different classifications, the accuracies of the dirt road and linear infrastructure were strongly related to the spatial resolution of the images through the influence of boundary pixels and influence of finer spatial resolution that increases the spectral-radiometric variation of land cover types [53].…”

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

“…Recently, a comprehensive review summarized different methods to detect paved roads from high resolution imagery and found generally high accuracies for machine learning techniques [21]. The literature review confirmed that a few studies achieved to extract the oil exploitation infrastructures and dirt roads using different methods through Landsat ETM+ and RapidEye data [22][23][24]. To our knowledge, there is no study on land cover classification of linear features which was conducted combining PlanetScope and RapidEye as high spatial resolution data with Landsat data providing a longer time series.…”