Onshore hydrocarbon seep detection using the GF-5 hyperspectral image: a case study in the Karamay area, NW China

Abstract

ABSTRACTSatellite remote sensing has been widely utilized for petroleum detection offshore since oil spill is normally widespread. However, the onshore hydrocarbon detection is insufficient because the lower spectral resolution of previous satellites made it difficult to identify small scales of seepages. In this study, a dataset of the High-Resolution 5 satellite (GF-5) of China was chosen to directly detect hydrocarbon seeps in the Karamay area. First, an IDL program was used to remove the vertical tripe noise of the image to ensure the high quality of the image. Second, use principal component analysis (PCA) was combined with the classification and regression trees (CART) method RuleGen to process the image. The detected hydrocarbon information was enhanced by RGB color synthesis. The results show that the methods can detect hydrocarbon effectively. Field verification results show that the accuracy is 86.5%. This study provides an effective method for hydrocarbon detection using GF-5 data.

Keywords

• Hydrocarbon seeps
• petroleum detection
• remote sensing
• image process