Spatio-statistical optimization of image segmentation process for building footprint extraction using very high-resolution WorldView 3 satellite data

Abstract

Segmentation process in building footprint extraction using object-based image analysis is crucial due to several factors, such as the spatial and spectral resolution of remote sensing images and the complexity of geo-objects. Consequently, the selection of suitable parameters to ensure the best segmentation quality remains a challenge. To overcome this issue, a spatio-statistical optimization technique that combines the Taguchi statistical method and a spatial plateau objective function (POF) was developed to extract building footprint from high-resolution Worldview 3 (WV3) satellite data. Initially, the Taguchi statistical method was used to design the orthogonal array of 25 experiments with three segmentation parameters, namely, scale, shape, and compactness, each having five varying values that directly affect the quality of segmentation. Asserted that, the scale factor was classified into small and large scales to avoid over-segmentation and under-segmentation problems. Afterwards, the POF, which is also a spatial optimization approach for evaluating segmentation quality, was computed for each experiment using their respective level combinations. Next, the combination of factor level in the orthogonal array and the calculated POF was merged to produce main effects and interaction plots for signal-to-noise ratios (SNR), whereby the smaller-is-better and larger-is-better options of the Taguchi’s SNR were tested on each parameter to maximize their effects. After that, the segmentation quality obtained from the proposed method was assessed by comparing with the benchmark method introduced by Dragut and result indicates that the proposed method was better than the benchmark method. Subsequently, the final optimal parameters were used for segmentation process in eCognition and the image object was classified into five land cover classes (building, road, water, trees, and grass) by using a supervised non-parametric statistical learning technique, support vector machine classifier. Finally, the building features was extracted, and the detection accuracy was evaluated based on receiver operating characteristics (ROC). Result shows the area under ROC curve (AUC) of 0.804 with p < 0.0001 at 95% confidence level. This verifies that the proposed method is effective for building detection with high accuracy and the integration of Taguchi and objective function managed to determine the optimal segmentation parameters. Optimization segmentation parameters can later be applied to distinguish roof materials and conditions.