Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data

ABSTRACT

Segmentation is the primary task for image analysis in many practical applications, such as object-based image analysis. Segmentation algorithms need to have properly estimated parameters to provide efficient performance and reliable results. Due to the fact that some features have different shapes and spectral characteristics, it is hard to find the proper parameters for the whole image. In this article, we propose a new method for resolving this issue through the building of a hierarchy of segmentations, based on the number of land-cover classes in the image, namely segmentation scale space (SSS). Both spectral and elevation data are employed in order to enhance the SSS and to obtain a single segmentation for the image. The performance of the proposed algorithm is evaluated using two data sets, which consist of ultra-high resolution aerial images and elevation data with ground sampling distance of 5 and 9 cm, respectively. The experiments demonstrate the efficiency of enhanced segmentation with respect to over and under segmentation cases. Finally, the comparative analysis shows that the accuracy of the proposed method is superior to the classical methods.

Acknowledgements

The authors would like to thank to Professor A. A. Ardalan, the Head of Hydrography Laboratory, University of Tehran, and Goodarz Yazdanpanah for providing the high performance processing unit for the experimental tests of this research. Special thanks also to the ISPRS work group III/4 and Dr Markus Gerke for making these valuable data sets available.

Disclosure statement

No potential conflict of interest was reported by the authors.