Urban major road extraction from IKONOS imagery based on modified texture progressing analysis technique

Abstract

A method for urban major road extraction from IKONOS imagery was proposed. The texture features of the image were first analyzed in three different levels. The first level calculated the Mahalanobis distance between test pixels and training pixels. The second level was the calculation results of Bhattacharyya distance between the distributions of the pixels in the training area and the pixels within a 3×3 window in the test area. The third level employed cooccurrence matrices over the texture cube built around one pixel, and then Bhattacharyya distance was used again. The processed results were thresholded and thinned, respectively. With the assistance of the geometrical characteristic of roads, the three resultant images corresponding to three levels were computed using fuzzy mathematics for their likelihood belonging to road and then merged together. A knowledge-based algorithm was used to link the segmented roads. The result was finally optimized by polynomial fitting. The experiment shows that the proposed method can effectively extract the urban major roads from the high-resolution imagery such as IKONOS.