Cropland Extraction Based on OBIA and Adaptive Scale Pre-estimation

Abstract

Object-based image analysis (OBIA) provides a solution for cropland extraction from high spatial resolution remote sensing images. Currently, scale parameter selection is often dependent on subjective trial-and-error methods or post-evaluation of multi-segmentation, which directly reduces efficiency of cropland extraction. This paper proposes a cropland extraction method combining spatial statistics based adaptive scale parameter pre-estimation and object-oriented classification. SPOT5 multi-spectral image in Baishan is used as experimental data to verify the validity of the methodology. Experimental results show that the pre-estimated scale parameter can yield a classification result with both high classification accuracy and completeness for extracting cropland information. This presented method avoids the time-consuming trial-and-error practice by accelerating the object-oriented classification procedure. Hierarchical rule set based classifications achieve higher accuracies and lower fragmentation than nearest neighbor-supervised classification. Additionally, this methodology can be rapidly transplanted into different regions and it is helpful for dynamic land-use monitoring and precision agriculture.