A Novel Linear Target Detection Method Based on Improved Probability Hough Transform in Remote Sensing Imagery

Linear target detection is one of the important courses in the artificial target recognition processing from the remote sensing imagery. The internal key technology is how to extract the long straight edges of the target from the background efficiently. Hough Transform is one of the classic methods for line detection. In view of the large scale and complex background in one remote sensing image, Standard Hough Transform (SHT) may generate too many short lines to distinguish the useful long linear targets. An improved method based on probabilistic Hough Transform (PHT) is proposed. Firstly, it divides the primary remote sensing image into sub-blocks and Canny operator is applied to detect edges inside each blocks. Then SHT is used to detect short lines and cluster them into groups to avoid most of the interfering lines. Finally, the parallel line pairs are extracted and filtered from the rest lines to confirm the long linear targets. Experiments show that the novel method can detect the linear target efficiently from the complex background.