Abstract
There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.
Highlights
It is necessary to emphasize that this procedure is detailed in [33,34] and that the weights used were the same as those obtained from the supervised training process of the Real-Coded Genetic Algorithm (RCGA) addressed on it
The reason for this was that in both cases the typology of used polygonal shapes presents very similar characteristics with regard to certain geometric properties used by the RCGA in order to carry out the classification
The computed Population distribution function (PDF) assesses the positional accuracy of the segmentation plots obtained with our Imagery Segmentation (IS) algorithm and represents the efficiency—from a positional perspective—with which this algorithm is able to segment an image
Summary
Imagery Segmentation (IS) is a longstanding problem in the computer vision field. IS could be defined as the process of dividing a certain image into regions—often referred to as regions of interest—which are homogeneous according to certain criteria [1]. These criteria can be very varied and are usually related to certain statistical properties of imagery, such as intensity value, tone, texture, etc. These regions must have a consistent meaning—
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