Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example

Geo-Object-Based Image Analysis (GEOBIA) is becoming an increasingly important technology for information extraction from remote sensing images. Multi-scale image segmentation is a key procedure that partitions an image into homogeneous parcels (image objects) in GEOBIA. Hierarchical image objects also provide a better representation result than a single-scale representation. However, scale selection in multi-scale image segmentation is always difficult for high-performance GEOBIA. This paper first generalizes the commonly used segmentation scale parameters into three aspects: spatial bandwidth (spatial distance between classes), attribute bandwidth (difference between classes) and merging threshold. Next, taking mean-shift multi-scale segmentation as an example, this paper proposes a spatial and spectral statistics-based scale parameter selection method for object-based information extraction from high spatial resolution remote sensing images. The main idea of this proposed method is to use the ALV graph to replace the semivariogram to pre-estimate the optimal spatial bandwidth. Next, the selection of the optimal attribute bandwidth and the merging threshold are based on the ALV histogram and simple geometric computation, respectively. This study uses Ikonos, Quickbird and aerial panchromatic images as the experimental data to verify the validity of the proposed scale parameter selection method. Experiments based on quantitative multi-scale segmentation evaluation testify to the validity of this method. This pre-estimation-based scale parameter selection method is practically helpful and efficient in GEOBIA. The idea of this method can be further extended to other segmentation algorithms and other sensor data.