Automatic selection of optimal segmentation scales for high-resolution remote sensing images

Yin, Ruijuan; Shi, Runhe; Gao, Wei

doi:10.1117/12.2021606

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The area-weighted variance (WV) and global Moran's I (MI) are often used to compute the homogeneity within objects and the heterogeneity between objects, respectively [29,30,38,39]. Higher WV values indicate the higher dispersion of spectral values and the lower homogeneity within the object.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%