Linear feature extraction using adaptive least-squares template matching and a scalable slope edge model

This paper presents a linear feature extraction method. Least squares template matching (LSTM) is adopted as the computational tool to fit the linear features with a scalable slope edge (SSE) model, which is based on an explicit function to define the blurred edge profile. In the SSE model, the magnitude of the grey gradient and the edge scale can be described by three parameters; additionally, the edge position can be obtained strictly by the 'zero crossing' location of the profile model. In our method the edge templates are locally and adaptively generated by estimating the three parameters via fitting the image patches with the model, accordingly the linear feature can be positioned with high accuracy by using LSTM. We derived the computational models to rectify straight line and spline curve features and tested those algorithms using the synthetic and real remotely sensed images. The experiments using synthetic images show that the method can position the linear features with the mean geometric error of pixel location of less than one pixel in certain noise levels. Examples of semiautomatic extraction of buildings and linear objects from real imagery are also given and demonstrate the potential of the method.