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.
Highlights
With the rapid development of remote sensing technology, high spatial resolution remote sensing images can be more obtained and widely used in a variety of applications [1,2,3,4]
We present a fast and effective unsupervised evaluation (UE) method for remote sensing images using the weighted variance (WV) and difference to neighbor pixels (DTNP)
From the H value, we can find that the change trend of DTNP and Moran’s I (MI) was more stable than between-segment heterogeneity (BSH)
Summary
With the rapid development of remote sensing technology, high spatial resolution remote sensing images can be more obtained and widely used in a variety of applications [1,2,3,4]. If the pixel-based analysis method which only uses the spectral information of the image is applied to the high-resolution image, its rich spatial information will be ignored and more noise will be produced [6,7]. Geographic object-based image analysis (GEOBIA) has begun to emerge and can achieve better accuracy in the high-resolution image [8,9,10]. In most of the segmentation algorithms, there is a parameter called “scale” to control the size of the object, which greatly affects the final segmentation results and the effect of GEOBIA [21,22,23,24]. Evaluating image segmentation is critical to select optimal scale and obtain better segmentation results for subsequent analysis
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