In this paper, a new automated approach for the extraction of aboveground circular storage structures from nearnadir very high resolution satellite imagery is proposed. The approach focuses on the cast shadows of the circular structures and splits the boundaries of the shadow regions into curved segments using the chord-to-point distance accumulation technique. Thereafter, the curved segments are tested with newly developed constraints for being a part of a circular structure, and the ones that pass all of the constraints are considered as candidates. The reciprocal relations between the candidate segments are assessed by a developed mutual evidence test, and for the candidates that expose a relation, a robust circle fitting is applied. For the candidates having no such relations, an approach that further validates the circle evidence is developed. The approach consists in introducing regions-of-interest (ROIs) for each candidate segment and applying a circular Hough transform in each ROI, where the parameters of the transform are self-controlled. Experiments performed on 12 challenging Geoeye-1 test images selected from industrial areas reveal that the proposed approach accurately detects aboveground circular structures in complex industrial environments. Besides, the comparison of the results of the proposed approach with the results of two different circle detection approaches verifies the success and the robustness of the approach developed.