Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis

Abstract

Most object-based image analysis use parameters to control the size, shape, and homogeneity of segments. Because each parameter may take a range of possible values, different combinations of value between parameters may produce different segmentation results. Assessment of segmentation quality, such as the discrepancy between reference polygons and corresponding image segments, can be used to identify the optimal combination of parameter values. In this research, we (1) evaluate four existing indices that describe the discrepancy between reference polygons and corresponding segments, (2) propose three new indices to evaluate both geometric and arithmetic discrepancies, and (3) compare the effectiveness of the existing and proposed indices in identifying optimal combinations of parameter values for image segmentation through a case study. A Landsat 5 Thematic Mapper (TM) image and an ALOS image of arid Northwestern China were used in the case study. The four existing indices include Quality Rate (QR), Over-segmentation Rate (OR), Under-segmentation Rate (UR), and Euclidean Distance 1 (ED1). The three proposed discrepancy indices include Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2). These indices measure overlap, over-segmentation, and under-segmentation between reference polygons and corresponding image segments. Results show that the three proposed indices PSE, NSR, and ED2 are more effective than the four existing indices QR, OR, UR, and ED1 in their ability to identify optimal combinations of parameter values. ED2 that represents both geometric (PSE) and arithmetic (NSR) discrepancies is most effective.