Building areas extraction basing on MSER in unmanned aerial vehicle images

Abstract

Automatic detection and extraction of the building area is an important aspect of unmanned aerial vehicle( UAV) image processing. Based on the detailed analysis of UAV imaging characteristics and the maximum stable extremal regions( MSER) algorithm,a building area extraction algorithm of UAV image is proposed. The algorithm consists of five steps: firstly,the pretreatment of UAV image; secondly,analysis and calculation of image stable regions using MSER; thirdly,screening the building area by calculating the density of stable regions; then,using adaptive K-means clustering algorithm to divide the building area; ultimately,boundaries of the building area were generated using Graham algorithm in order to achieve automatic extraction of building area. Using the UAV real flying image data to do the experiment statistics,the conclusion includes: Firstly,the extraction accuracy of this algorithm reaches 92. 25%; secondly,when compared with other building area extraction algorithm which based on Gabor transform or SIFT,the extraction time of building area is shortened and meets the needs of UAV real-time applications.