Unmanned Aerial Vehicle Remote Sensing Image Segmentation Method by Combining Superpixels with multi-features Distance Measure

Abstract

Image segmentation is the foundation and key step of object-level classification and change detection. In this paper, a segmentation method of UAV remote sensing image based on multi-features distance measure and superpixels is proposed. First, the simple linear iterative clustering (SLIC) algorithm is used to segment the unmanned aerial vehicle (UAV) remote sensing image to obtain the initial superpixels. Then the distance measures of the spectral, texture, shape and area features are used as the criterion for initial superpixels merging. Finally, merger termination when the number of regions reaches the set number. Two groups of UAV remote sensing images are selected to evaluate the experimental results through visual evaluation. The experimental results show that the proposed method can be used to aggregate objects of different scales, and the segmentation effect is satisfactory.