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1. UNSUPERVISED QUANTIFICATION OF UNDER- AND OVER-SEGMENTATION
FOR OBJECT-BASED REMOTE SENSING IMAGE ANALYSIS
ABSTRACT
Object-based image analysis (OBIA) has been widelyadopted as a common paradigm to
deal with very high-resolutionremote sensing images. Nevertheless, OBIA methods
stronglydepend on the results of image segmentation. Many segmentation quality metrics have
been proposed. Supervised metrics give accuratequality estimation but require a ground-truth
segmentation asreference. Unsupervised metrics only make use of intrinsic imageand segment
properties; yet most of them strongly depend on theapplication and do not deal well with the
variability of objects inremote sensing images. Furthermore, the few metrics developed ina
remote sensing context mainly focus on global evaluation. In thispaper, we propose a novel
unsupervised metric, which evaluateslocal quality (per segment) by analyzing segment
neighborhood,thus quantifying under- and over-segmentation given a certainhomogeneity
criterion. Additionally, we propose two variants ofthis metric, for estimating global quality of
remote sensing imagesegmentation by the aggregation of local quality scores. Finally,we analyze
the behavior of the proposed metrics and validate theirapplicability for finding segmentation
results having good tradeoffbetween both kinds of errors.