Optimal segmentation of a high-resolution remote-sensing image guided by area and boundary

Image segmentation is the premise of object-based image analysis (OBIA), and obtaining an optimal segmentation result has been a desire for many researchers. This article proposes an optimal segmentation method for a high-resolution remote-sensing image that is guided by spatial features of area and boundary. This method achieves an optimal result through stepwise refinement on multi-scale segmentations. First, boundary strength is integrated into the choice for the optimal scale based on an improved unsupervised evaluation. Then, under-segmented objects (USOs) and over-segmented objects (OSOs) at the selected optimal scale are identified using a heterogeneity histogram and a slider-like threshold with the guidance of area and boundary. Finally, the corresponding objects, in a specific finer segmentation, are taken to replace the USOs at the optimal scale, and then the USOs and OSOs are refined by an effective merging mechanism. A heterogeneity-change-based merging criterion considering boundary, shape, spectral, and texture features is constructed for the merging of neighbouring objects. The proposed method is more effective than the unsupervised image segmentation evaluation and refinement (UISER) method as it uses spatial features to guide optimal choice of scale, and USO and OSO identification and refinement. Comparative experiments show that the spatial features used in the proposed method are effective for achieving an enhanced segmentation result.