Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data

Abstract

Although much efforts have been made to develop automatic methods for building extraction from very high-resolution (VHR) imagery during the past 30 years; the methods with high performance are still unavailable due to the three issues: uncertainty of segmentation scales, selection of effective features, and sample selection. In this study, by introducing GIS data, a parameter mining approach is proposed to (1) mine parameter information for building extraction, and (2) detect changes of buildings between VHR imagery and GIS data. For the first target, the learning mechanism is proposed for identifying optimal segmentation scales, feature subsets, and samples. For the second target, the discovered information (i.e., optimal segmentation scales, feature subsets, and selected samples) is applied to classify the VHR imagery with a multilevel random forest (RF) classifier. The proposed approach is validated on two datasets: Dataset 1 and Dataset 2. The knowledge of building extraction is first learned from Dataset 1 and then used to classify both datasets, and change detection is conducted on Dataset 1. Results of change detection in Dataset 1 indicate that the false alarm ratio and omission error of increased buildings are 20.1% and 8.4%, while the false alarm ratio and omission error of destroyed buildings are 19.1% and 11.3%, respectively. Results of building extraction in Dataset 2 revealed scores of 81.50% and 81.09% at pixel- and object-based evaluation levels. Accordingly, our proposed method is successful in building extraction and change detection.