In this paper, a new method is proposed for airport recognition in complex environments. The algorithm takes all
advantages of essential characteristics of the airport target. Structural characteristics of the airport are used to establish
assumption process. Improved Hough transformation (HT) is used to check out those right straight-lines which stand for
actual position and direction of runways. Morphological processing is used to remove road segments and isolated points.
Finally, we combine these segments carefully to describe the whole airport area, and then our automatic recognition of
airport target is realized.
In this paper, a new method is proposed for road extraction in complex environments. The algorithm takes full
advantages of the road image characteristics. Firstly, the image is preprocessed and the object edges are extracted. Then,
by dividing the image into many blocks (such as m×m), an improved Radon transform (RT) is performed to extract the
line segments in images. We do Radon transformation for each block. To reduce the computing time, a threshold is set
and images are resumed along the ribbon region of transformation domain. Experiments show that the presented method
can extract roads even in complex environments, and moreover, it can provide a complete description of the road.
When roads appear as irregular linear targets, most of traditional methods which use local information often give a false
alarm of linear feature. In order to overcome this disadvantage, we present an approach to extract main roads in aerial
images with oriented filters. Firstly, the leading gradient direction of the local areas is estimated. Then the image is
filtered along this direction. We use steerable filters to control the output and get the maximum output response along the
road direction. Based on the steps above, we make much improvement in details such as the determination for the size of
the sub-block image, the processing of image edge pixels and the processing of orientation angel areas and so on.
Experiments on remote sensing images of different environmental settings show that our proposed scheme achieves high
accuracy in the road extraction.
In this paper, by analyzing the basic road features in remote sensing images, the model of road extraction is discussed.
The popular methods of road extraction and their advantages and disadvantages are generalized. The recent progress and
results of our research group at relative aspects are introduced. The development of the issue is also presented.
In this paper, a new method to recognize bridge in the complicated background is presented. The algorithm takes full
advantages of the characteristics of the bridge image. Firstly, the image is preprocessed and the object edges are
extracted. Then according to the limitations of traditional Hough transform (HT), the extraction method of the image line
segment characteristic of HT is improved, which eliminates spurious peaks on the basis of global and local thresholds,
discriminates the position relation between two straight line segments, and merges segments with near endpoints, etc.
Experiments show that this algorithm is more precise and efficient than traditional HT, moreover it can provide a
complete description of the bridge in a complicated background.