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

机译：基于BAT算法和归一化色差的遥感图像特征提取

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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%.
机译：这些准确地提取遥感图像中的特定对象已成为一个研究热点。对于遥感图像特征提取，可以选择形状，颜色和其他功能以从复杂场景中提取对象。本文提出了一种基于BAT算法和归一化色差的遥感图像特征提取方法。首先，使用BAT算法增强了遥感图像的对比度。在增强之后，与传统直方图均衡相比，从直方图可以看出优化的图像对比度显着增强。然后，采用归一化的色差方法提取特征。通过归一化RGB三通道组分并与固定阈值进行比较来计算归一化色差。最后，获得特征二进制图，然后提取遥感图像中的感兴趣区域（ROI）。本文提出的算法可以实现远程远程信息处理检测图像处理，并获得完整和准确的目标区域。最高提取率达到96％。

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