A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery

Abstract

The results obtained using the object-based image analysis approach for remote sensing image analysis depend strongly on the quality of the segmentation step. In this paper, to optimize the scale parameter in a multiresolution segmentation, we analyse a high-resolution image of a large and heterogeneous agricultural area. This approach is based on using a set of agricultural plots extracted from official maps as uniform spatial units. The scale parameter is then optimized in each uniform spatial unit. Intra-object and inter-object heterogeneity measurements are used to evaluate each segmentation. To avoid subsegmentation, some oversegmentation is allowed, but is attenuated in a second step using the spectral difference segmentation algorithm. The statistical distribution of the scale parameter is not equal in all land uses, indicating the soundness of this local approach. A quantitative assessment of the results was also conducted for the different land covers. The results indicate that the spectral contrast between objects is larger with the local approach than with the global approach. These differences were statistically significant in all land uses except irrigated fruit trees and greenhouses. In the absence of subsegmentation, this suggests that the objects will be placed far apart in the space of variables, even if they are very close in the physical space. This is an obvious advantage in a subsequent classification of the objects.

Keywords:

• object-based image analysis
• segmentation
• optimization
• eCognition^TM

Acknowledgements

We would like to thank the CARM Environmental Integration and Management Service (SIGA) for providing us with the imagery of our study area and the General Direction of the Common Agriculture Policy (CARM agricultural authority) for providing us with the SIGPAC data. We also thank the two anonymous reviewers whose suggestions have substantially improved this manuscript.

Additional information

Funding

This study was carried out within the framework of 15233/PI/10, funded by Fundación Séneca. This research has been also partly funded by Prometeo Project, Secretariat of Higher Education, Science, Technology and Innovation, Gobierno de Ecuador.