Road extraction in complex scenes for high-resolution remote sensing images is mostly performed using the supervised classification method. However

this method requires the manual selection of samples

thereby leading to low degree of automation and instability. The pixel-based extraction method exhibits low completeness and can easily produce salt-and-pepper noise. The object-oriented method can easily produce adhesion problem. In this study

a road extraction method based on unsupervised classification and geometrical-textural-spectral features (i.e.

unsupervised classification and fused features

UCFF) is proposed to improve the completeness

accuracy

and automation of road extraction. In the UCFF method

the unsupervised classification avoids the artificial sample selection steps; its fusion of spectral

geometrical

and textural features produces extraction results of high accuracy. The proposed UCFF road extraction method still presents high completeness and accuracy while improving the degree of automation. The UCFF method mainly includes three parts:the road candidate area extraction

non-road area filtering

and road centerline extraction. The road candidate area extraction involves two steps. First

spectral features are adopted for unsupervised classification. The ISODATA method is used in the unsupervised classification. However

the non-road area of similar spectral features is easily separated into roads by the unsupervised classification. Second

another classification based on textural feature is applied. Given that most of the non-road and road areas significantly differ in textural features

the classification based on textural feature can be used to distinguish road area from the non-road area with similar spectral features. The textural feature used is the variance of four different directions:horizontal

vertical

diagonal

and back-diagonal. A 3×3 sliding window with a center pixel is used to calculate the variance. When all the variances in four directions are less than the threshold

the center pixel is considered the road. By combining the two classification results

the road candidate region is obtained. For the non-road area filtering

edge

texture

and shape filters are gradually applied. The edge filter is used to disconnect road and non-road connections. A Canny method is performed to extract the edge. The roads are considered not on the edge. If a point in the image is an edge

then it is not the road. The textural feature is used to filter large non-road area that is wider than the road

such as large parking lots and buildings. In the binary image

the road is narrow and is a long connected area. A window that is slightly larger than the width of the road is selected. In this window

the textural feature value of the non-road area is less than that of the road area. When the textural feature value is less than the threshold

the center pixel is considered the non-road and should be filtered. Finally

a shape index (i.e.

linear feature index) is used to filter the remaining small non-road area and thus extract the final road area. For the road centerline extraction

a tensor voting algorithm is used. Experiments on high-resolution remote sensing images are conducted to verify the effectiveness of the proposed method. High-resolution IKONOS images of Australia's Hobart region and QuickBird images of Texas

USA

in complex scenes are employed. Another two typical pixel-based and object-oriented road extraction methods at home and abroad are compared to further prove the feasibility and effectiveness of the proposed method. Three indexes of completeness

accuracy

and detection quality are used for quantitative evaluation. Quantitative analysis shows that the three indexes of the proposed method are averagely 26.61%

5.57%

and 26.77% higher than those of the two other algorithms. The qualitative analysis also shows that the proposed method can effectively suppress the salt-and-pepper noise and adhesion phenomena. The method also presents high automation capability. The proposed road extraction method can be used mainly in complex scenes for high-resolution remote sensing images. The preliminary classification under unsupervised conditions can avoid the steps of sample selection in an automatic manner. The spectral feature is considered in the unsupervised classification

while the textural feature of road is fully utilized in the texture classification. Thereafter

the two road classification results are combined as the candidate region to effectively avoid the adhesion and salt-and-pepper noise phenomena. The road extraction results are smooth and complete. In the non-road filter stage

the specific nature of the road is fully used

which combines the edge

shape

and textural features to achieve the accuracy of road extraction. A road extraction method for high-resolution remote sensing images based on unsupervised classification and geometrical-textural-spectral features is proposed. The unsupervised classification method possesses higher degree of automation than that of the supervised classification method. Road extraction under complex scenes based on geometrical-textural-spectral features can effectively avoid the salt-and-pepper noise and adhesion phenomena while obtaining high completeness and accuracy. This method is suitable for road extraction in complex scenes

such as cities

and achieves high completeness

accuracy

and automation. Unsupervised classification and multiple feature combination have broad application prospects in the road extraction. The theory can provide reference for other road extraction in future studies.