A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

Abstract

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning

“…The minsize parameter also referred to as the scale parameter, which determines the relative segment size ( Drǎguţ et al., 2010 ), has a substantial impact on OBIA and classification results ( Zhao et al., 2020 ; Hao et al., 2021 ). Possibly, setting a smaller minsize parameter reduces the probability of segmenting multiple classes into a single object, leading to increased overall accuracy.…”

“…Both qualitative evaluation through visual inspection and quantitative evaluation using reference data are integral components of the accuracy assessment in OBIA ( Zhao et al., 2020 ). In supervised classification, a confusion matrix is a commonly used tool to organize information essential for accuracy assessment ( Bratic et al., 2018 ).…”

Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images

“…We considered this step negligible because the objective of this work now is the creation of a logical scheme that, by combining the membership functions applied to the different features, leads to a correct classification and distinction of cultivated and uncultivated fields. To further increase the quality of the classification, more rigorous quality controls could be applied to the segmentation obtained [18,19] and consequently calibrate the various factors that influence it to maximize the quality of the segmentation.…”

Table_1.docx

“…The minsize parameter also referred to as the scale parameter, which determines the relative segment size (Drǎguţet al, 2010), has a substantial impact on OBIA and classification results (Zhao et al, 2020;Hao et al, 2021). Possibly, setting a smaller minsize parameter reduces the probability of segmenting multiple classes into a single object, leading to increased overall accuracy.…”