Introductory remarks

The extraction of line features from two-dimensional digital images has been a topic of considerable interest in the past two decades due to its numerous applications in astronomy, remote sensing, and medical fields. For example, typical problems in astronomy are the extraction of streaks corresponding to the trajectory of meteorites or satellites in space. In remote sensing, a major concern is to decipher from satellite images the network of roads and the separation among fields in agriculture. A common problem in both cases is the nature of the scene itself, which is often noisy with a complex background structure.

Among the techniques used in line extraction are those based on the matched filter concept. Under favorable noise conditions, namely, high SNR and i.i.d. Gaussian samples, the matched filter performs well. Unfortunately, typical noise conditions differ from the Gaussian distribution or, even if Gaussian, their variance is unknown. In addition, the SNR is usually not too high and varies unpredictably over the scene under consideration. As a result, a simple thresholding scheme will fail under these conditions.

Finally, the presence of structured backgrounds such as clouds or smoke will often hide parts of the line, and it is important to remove the background interference without affecting the discontinuity of the line.