Feature Extraction of Remote Sensing Images Based on Bat Algorithm and Normalized Chromatic Aberration

作     者：Yi Cao Yuting Xun Yu Han Jian Chen Shubo Wang Zichao Zhang Nannan Du Hao Meng 

作者机构：College of Engineering China Agricultural University Beijing 100083 China College of Water Resources & Civil Engineering China Agricultural University Beijing 100083 China Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment Beijing China 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2019年第52卷第24期

页      面：318-323页

主　　题：remote sensing image bat algorithm normalized chromatic aberration feature extraction 

摘      要：These accurate extraction of specific objects in remote sensing images has become a research hotspot. For remote sensing image feature extraction, shape, color and other features can be selected to extract objects from complex scenes. In this paper, a method of remote sensing image feature extraction based on bat algorithm and normalized chromatic aberration is proposed. Firstly, the contrast of remote sensing images is enhanced by using bat algorithm. After enhancement, it can be seen from the histogram that the optimized images contrast is significantly enhanced compared with the traditional histogram equalization. Then, the normalized chromatic aberration method is adopted to extract features. The normalized chromatic aberration is calculated by normalizing the RGB three-channel component and compared with the fixed threshold. Finally, the feature binary graphs are obtained, and then the region of interest (ROI) in the remote sensing image is extracted. The algorithm proposed in this paper can realize remote telematics sensing images processing and obtain complete and accurate target areas. The highest extraction rate was reached 96%.