Abstract
Compared to multispectral or panchromatic bands, fusion imagery contains both the spectral content of the former and the spatial resolution of the latter. Even though the Estimation of Scale Parameter (ESP), the ESP 2 tool, and some segmentation evaluation methods have been introduced to simplify the choice of scale parameter (SP), shape, and compactness, many challenges remain, including obtaining the natural border of plastic greenhouses (PGs) from a GaoFen-2 (GF-2) fusion imagery, accelerating the progress of follow-up texture analysis, and accurately evaluating over-segmentation and under-segmentation of PG segments in geographic object-based image analysis. Considering the features of high-resolution images, the heterogeneity of fusion imagery was compressed using texture analysis before calculating the optimal scale parameter in ESP 2 in this study. As a result, we quantified the effects of image texture analysis, including increasing averaging operator size (AOS) and decreasing greyscale quantization level (GQL) on PG segments via recognition of a proposed Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern. The proposed pattern can be used to reasonably evaluate the quality of PG segments obtained from GF-2 fusion imagery and its derivative images, showing that appropriate texture analysis can effectively change the heterogeneity of a fusion image for better segmentation. The optimum setup of GQL and AOS are determined by comparing CEI and visual analysis.
Highlights
Extracting plastic greenhouse (PG) segments from well-segmented high-resolution imagery is a basic goal for many applications, such as area monitoring, production forecast, and the accurate inversion of land surface temperature; and it is more effective than traditional manual drawing when many samples have to be selected as the reference polygons in large-scale research.Segmentation, its evaluation, and texture analysis are crucial steps in geographic object-based image analysis (GEOBIA)
To better understand the problems in PG segmentation, the definitions of variables, and the establishment of Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern, five cases of PG segments that were extracted from initial GF-2 fusion imagery and four derivative images are demonstrated in Figure 4, and all images were segmented under their optimal scale parameter provided by the Estimation of Scale Parameter (ESP) 2 tool
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Summary
Extracting plastic greenhouse (PG) segments from well-segmented high-resolution imagery is a basic goal for many applications, such as area monitoring, production forecast, and the accurate inversion of land surface temperature; and it is more effective than traditional manual drawing when many samples have to be selected as the reference polygons in large-scale research. Even though thematic vector data can improve the quality of the segmentation [19], the decision of the optimal value of scale parameter (SP), shape, and compactness in MRS is not easy, since the conventional try-and-evaluate method [19,20] is too complicated, time consuming, and provides incomplete results. Textural information can be used as an additional band to improve the object-oriented classification of urban areas in Quickbird imagery [25]; a similar pixel-based maximum likelihood PG classification in Agüera et al.’s research [26] showed that the inclusion of a band with texture information did not significantly improve the overwhelming majority quality index values compared to those found when only multi-spectral bands were considered. The study area (36◦44 40”N and 118◦49 0”E) was chosen for these reasons: (a) greenhouses are the main local production mode and are developing rapidly in Shouguang City; (b) even though the greenhouses account for nearly half the area in the selected region, they are adjacent to various land cover types such as water, trees, buildings with high reflectance, residences, and barren land, which form a representative common image; and (c) both continuous and scattered greenhouse can be found in the selected region
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