Road Detection Method for Land Consolidation Using Mathematical Morphology from High Resolution Image

Land consolidation is a tool for increasing the area of the arable land and improving the effectiveness of land cultivation. With the development of high resolution image, the progress of land consolidation project can be monitored by acquiring information from the image objectively. This paper presents an algorithm to detect roads in land consolidation project from high resolution images. The method is based on the mathematical morphology, which is a method for detecting image components that are useful for representation and description. The vector planning maps and high resolution images used to monitor the completion of land consolidation project are registered. The candidate road areas are created using the functions of buffer and extraction by mask in GIS. Top-hat transform and gray dilation are used to filter the noise of the image. In this way the road feature in the image became wider and even more obvious to be recognized. Then image binarization and thinning algorithm are used to extract the one-pixel centerline of the road. At last, the thinning results are converted to the final vector detection results.