A New Clustering Algorithm Toward Building Segmentation From Aerial Images by Utilizing RGB‐Component Differences

Abstract

Abstract For the image segmentation task, deep features‐based deep learning methods have strict data requirements on the quality and quantity, which limits their application in irregular scenes, such as the villages subjected to geological hazards in the complex mountains. However, traditional algorithms with shallow and middle features are unable to utilize the complex information of unmanned aerial vehicle (UAV) remote sensing images effectively. To address this issue, we proposed a new image segmentation algorithm by using the RGB component difference clustering (RGBCDC). In this method, after dimensionality reduction of RGB color space from three dimensions to two dimensions, the distance between similar objects would be diminished and the distance between disturbance areas would be enlarged. Two different UAV data sets (3,000 in total) have been used to compare the proposed algorithm with the traditional segmentation algorithms and the deep learning‐based methods. Results show that the average pixel accuracy is 13.11%, 11.86%, 13.84%, 8.00%, and 9.86% higher than Octree Quantization clustering algorithm, Region Growing algorithm, Watershed algorithm, K‐Means algorithm, and SLIC superpixel segmentation algorithm, respectively. When facing insufficient samples, the new method performed better than deep learning algorithms, such as Deeplabv3, PSPnet, UNET, and UISBB. In general, the proposed algorithm for building segmentation shows a better potential for use in uncommon situations, especially for rapid emergency rescue after serious mountain hazards.

Keywords

• UAV remote sensing image
• building segmentation
• RGB component difference
• clustering