Enhancing linear features in image data using horizontal orthogonal gradient ratios

Horizontal directional derivatives and sunshading are two commonly used gradient-based filters for enhancing linear features in potential field data. The Tilt angle is a useful filter that produces an amplitude-balanced vertical derivative. The disadvantage of the Tilt angle-based filters is that they require the computation of the vertical derivative of the data, which as well as making the filters more computationally intensive, limits their application to datasets where this is physically meaningful. This paper presents amplitude-balanced horizontal derivatives that do not require the vertical derivative of the field and thus are faster and have general application.