Color constancy algorithm based on local exact histogram matching for scene matching navigation of UAVs

To address the color variability issues existing between the sensed images and the reference images in scene matching navigation of unmanned aerial vehicles (UAVs), a color constancy algorithm is proposed based on local exact histogram matching (LEHM). The sensed image is divided into a set of sub-images, and the erroneous correspondence between the sensed image and the reference image is obtained according to the location and attitude of UAV predicted by the scene matching aided navigation system. The shifting, scaling and rotation compensation are taken into account to select the sub-images from the reference image corresponding to the sub-images from the sensed image. Three intensity mapping relationships are established from the histograms of each pair of sub-images in red, green, and blue channels. At last, the three color channels of the sub-images from the sensed image are adjusted in accordance with the established intensity mapping relationships. This will reduce the color differences between the sensed image and the reference image. Experimental results show that the euro angle error of the estimated illuminant color using the proposed algorithm is reduced by 80% compared with those by traditional color constancy methods. Moreover, the mean scene matching error is reduced by almost 70% in comparison to the scene matching algorithm without color constancy processing. By introducing the blocking scheme, the proposed algorithm could overcome the color variability issues caused by multi-illuminant imaging and possess the potential of parallel computation, which is suitable for engineering applications.