Analysis of large-scale UAV images using a multi-scale hierarchical representation

Abstract

Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.