Automatic Extraction of Corner Points from Aerial Images Using Point-Feature Operators and Hough Transform

Abstract

Low-level feature extraction such as lines and points (i.e. corners), forms a fundamental step in digital photogrammetry and other fields. They supply the inputs for the photogrammetric orientation procedures; and they serve as an intermediate input for other processes such as object recognition. With the accumulation of knowledge, the research community is in a better position to develop new generations of smart algorithms and solutions that possess a new level of maturity and understanding for the underlying challenges of automation. To this end, this paper presents an innovative approach for corner point extraction that combines the outputs from classical point feature operators with Hough Transform to generate a better hypothesis for a corner point that can be used for applications in urban areas. In particular, extracted point features were used to guide line extraction in a local neighbourhood by Hough Transform. Then the corner points that will be obtained from lines intersection in this local neighbourhood will be compared with their nearby ones that were extracted by point feature operators. Based on passing a set of criteria, the intersection points from lines will replace the point feature as aset of potential corner points. Experimental findings show promising results of the proposed approach that raises the confidence level of the extracted corners and eliminating outliers.