An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems
نویسندگان
چکیده
Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and intensification assessments. However, the inherently high landscape fragmentation irregularly shaped associated with smallholder farming systems restrict accuracy of parcels extraction. In this study, we proposed an adaptive image segmentation method automated selection optimal scale (MSAOS) to extract in heterogeneous landscapes. The MSAOS includes three major components: (1) coarse divide whole images into homogenous regions, (2) fine determine based on average local variance function, (3) region merging merge dissolve over-segmented objects small area. potential derived from were combined random forest generate final parcels. was evaluated over different regions China, results assessed by benchmark interpreted high-spatial resolution images. Results showed texture features Homogeneity Entropy most important parcels, highest separability index 0.28 0.26, respectively. MSAOS-derived had agreement reference dataset eight tiles Qichun county, F1 scores 0.839 0.779 area-based classification evaluation (Fab) object-based (Fob), further four provinces exhibited similar (Fab = 0.857 Fob 0.775) that test tiles, suggesting good transferability regions. Furthermore, outperformed other widely-used approaches terms integrity extracted These indicate great using conjunction effectively systems.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14133067