Object-Based Land Cover Mapping using Adaptive Scale Segmentation from ZY-3 Satellite images

With increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. However, scale selection in multi-scale segmentation and OBIA remains a challenge, which directly reduces efficiency of land cover mapping. In this study, we presented an object-based land cover mapping using adaptive scale segmentation. Central to our method is the use of inherent features of segmented objects to determine whether an object should be segmented with a small scale in a top-down segmentation procedure. We firstly used inherent features of a segmented object to determine whether this object should be segmented with a smaller scale in a top-down segmentation procedure, producing a segmentation map with optimal scales. Then, an object-based SVM classifier was applied on the adaptive-scale segmentation map to yield a land-cover map. We have applied this method on a ZY-3 multi-spectral satellite image to produce land cover map, compared with the results using the traditional mean shift algorithm with fixed scales. The experimental results illustrate that the proposed method is practically helpful and efficient to improve the performance of land cover mapping.