Traditional line feature detection methods based on structureless algorithms of the Beamlet transform not only suffer from overlapping and ambiguities

they also can not detect the target information effectively. Moreover

they can not describe the detail information when extracting the line features of a complex image. Therefore

we propose a new line feature extraction algorithm based on an improved Beamlet transform and the Canny operator. First

the Beamlet transform is performed. There is at most one optimal Beamlet in a dyadic square after improving the Beamlet structureless algorithm and using the new drawing rule and the new energy function. Second

the Canny operator for edge detection is used with a larger Sigma in order to detect only obvious edges. Finally

line feature are detected by a combination of both. The algorithm is evaluated under several aspects

such as the continuity of the line feature extraction

the false detection rate and the miss detection rate. Moreover

this method is compared to existing methods. The experimental results show that our proposed method not only overcomes their weakness such as fractureing

overlapping

sambiguities

false edges and so on

but also effectively improves the accuracy and continuity when extracting line feature of complex image.