Robust approach for suburban road segmentation in high-resolution aerial images

    Authors

    D. Guo; A. Weeks;H. Klee

    Comments

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    Abbreviated Journal Title

    Int. J. Remote Sens.

    Keywords

    road segmentation; USGS; high-resolution aerial photos; NETWORK EXTRACTION; SNAKES; Remote Sensing; Imaging Science & Photographic Technology

    Abstract

    The goal of this research is to develop an algorithm that accurately segments high-resolution images, where linear features, such as roads, are corrupted by noise. In high-resolution images, there are two types of noises that are obstacles to road segmentation. Noise could be within road areas (such as cars, water, differences in composite road surface mix) or unwanted contents outside road areas, like buildings and trees. To remove unwanted contents, the geographical information from the United States Geographical Survey (USGS) is used. The USGS provides a collection of road centre line information that has been collected for many years and can be used to limit the area for road segmentations close to roads. In this paper, a standard process was developed to align the USGS geographical information with the high-resolution images. USGS geographical data is used to eliminate background clutter that is disjunct from roads. The road segmentation process is then reduced to dealing with automobile traffic, shadows and pavement colour discontinuity within road areas. In order to achieve reliable road segmentation in the presence of these objects, the mean-shift clustering approach is used within the hue-saturation-intensity (HSI) space. Conditional morphological image processing techniques are also used to significantly improve the segmentation results. The proposed method results in the average accuracy of road segmentation above 85%.

    Journal Title

    International Journal of Remote Sensing

    Volume

    28

    Issue/Number

    1-2

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    307

    Last Page

    318

    WOS Identifier

    WOS:000244093200018

    ISSN

    0143-1161

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