High Consequence Area identification method based on high-resolution remote sensing images

Abstract

Presently, the High Consequence Areas (HCAs) are mainly identified according to Oil and gas pipeline integrity management specification (GB 32167－2015). However, due to the lack of effective technical means, the quantitative identification criterion in the standard has a limited guidance effect on the actual identification work, thus leading to the inefficiency of identification and the low accuracy of identification results. In this regard, the HCA identification method based on high-resolution remote sensing images was proposed. Meanwhile, the buildings along the pipeline were detected and extracted automatically based on the convolutional neural network and the conditional random field, in combination with the identification of buffer area and the Linear Referencing based pipeline segmentation. Thus, the identification of HCAs along the pipeline was completed through the geometric calculation. Moreover, the HCAs of a pipeline were identified with this method, of which the accuracy rate and recall rate were 93% and 90%, respectively, showing a great improvement in the efficiency and accuracy of HCA identification. Hence, the research results could provide technical guidance to the efficient and quantitative identification of HCAs along the pipeline, and the method has a good application prospect.

Keywords

• long-distance pipeline
• high-resolution remote sensing images
• high consequence area (hca)
• convolutional neural network
• conditional random field
• identification