Multi-scale image segmentation and object-oriented processing for land cover classification

Abstract

The purpose of this research is to evaluate the utility of image segmentation and object-oriented processing for land cover classification in Ohio. Eight level-II land cover categories were classified using a region-growing segmentation algorithm and object-oriented fuzzy classification membership functions. The overall accuracy of the classification was 93.6%. Producer accuracies ranged from 89.63% for urban/recreational grasses to 98.01% for water. User accuracies ranged from 90.83% for deciduous forest to 99.49% for water. The high classification accuracies are primarily due to: (1) the use of multiple scales in the segmentation process for classification of small to large phenomena at the appropriate scale; (2) integration of textural, contextual, shape, and spectral information in the classification process; and (3) use of multi-temporal data to capture both leaf-on and leaf-off properties of land cover categories.