Abstract
Image segmentation is a crucial stage at the very beginning of many geographic object-based image analysis (GEOBIA) workflows. While segmentation quality is generally deemed of great importance, selecting adequate tuning parameters for a segmentation algorithm can be tedious and subjective. Procedures to automatically choose parameters of a segmentation algorithm are meant to make the process objective and reproducible. One of those approaches, and perhaps the most frequently used unsupervised parameter optimization method in the context of GEOBIA is called the objective function, also known as Global Score. Unfortunately, the method exhibits a hitherto widely neglected, yet severe source of instability, which makes quality rankings inconsistent. We demonstrate the issue in detail and propose a modification of the Global Score to mitigate the problem. This hopefully serves as a starting point to spark further development of the popular approach.
Highlights
Image segmentation is one of the first stages in geographic object based image analysis (GEOBIA).It is performed with the objective to partition an image into meaningful groups of pixels, i.e., the geo-objects depicted in an image
We use Multiresolution Segmentation (MRS) to illustrate our research, it has to be noted that the choice of segmentation algorithm itself does not affect the findings of our study
Correlation between adjacent regions initially declines with growing size, but is expected to increase when segments become large enough to contain a
Summary
It is performed with the objective to partition an image into meaningful groups of pixels, i.e., the geo-objects depicted in an image. The quality of the segments is deemed crucial, as it will affect the performance of subsequent processing, especially the possibility to assign meaningful class labels to objects [1]. Image segmentation is regarded a hard problem in computer vision, due to its ill-posed nature [2,3]. By changing a segmentation algorithm’s tuning parameters or by altering the pre-processing of the input imagery, it is possible to produce a vast number of different segmentations for an image. Checking a large number of candidate solutions is possible and potentially leads to satisfying results [4], but is inherently time consuming and subjective in nature
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