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

Abstract 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%.