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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：这些对遥感图像中特定物体的准确提取已成为研究热点。对于遥感图像特征提取，可以选择形状，颜色和其他特征以从复杂场景中提取对象。提出了一种基于蝙蝠算法和归一化色差的遥感图像特征提取方法。首先，利用蝙蝠算法增强了遥感图像的对比度。增强后，从直方图中可以看出，与传统的直方图均衡化相比，优化后的图像对比度得到了显着增强。然后，采用归一化色差方法提取特征。通过对RGB三通道分量进行归一化计算出归一化色差，并将其与固定阈值进行比较。最后，获得特征二值图，然后提取遥感图像中的关注区域（ROI）。本文提出的算法可以实现远程信息处理的遥感图像处理，并获得完整，准确的目标区域。最高提取率达到96％。

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