Self-organised clustering for road extraction in classified imagery
نویسندگان
چکیده
The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and Ž . Ž . inefficiency to meaningfully exploit multispectral imagery MSI . With a ground sample distance GSD of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge Ž . definition. A self-organising road map SORM algorithm is presented, inspired from a specialised variation of Kohonen’s Ž . self-organising map SOM neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input. q 2001 Elsevier Science B.V. All rights reserved.
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تاریخ انتشار 2001