Unbiased Histogram Matching Quality Measure for Optimal Radiometric Normalization

Radiometric normalization is critical for multi-spectral image change detection. In this paper, a histogram matching method is proposed to perform relative radiometric normalization among heterogeneously sensed images. To quantify the histogram matching quality, which is reference image and band dependent, the image differencing based quantitative measure, such as Euclidean or Manhattan distance, was proposed. However, when the image difference based measure is used to optimize the reference image and band for the best histogram match, it is always biased to the reference image with the histogram compacting at the lower bits. To overcome this problem, image preprocessing, such as histogram equalization, mean standard deviation normalization or image bit clipping can be used to spread the histograms to the full dynamic range and thus eliminates the bias effect. However, this significantly increases the computational burden. In this paper, a new unbiased symmetric image pixel ratio is proposed as a measurement criterion for the histogram matching quality measurement. This measure consistently picks one of two relative ratios of every pixel pair of the reference image and the histogram matched subject image, which is consistently either less than or greater than 1 as selected; and the average of the ratios over the image reflects the goodness of the match. The proposed new measure is experimentally compared with the Manhattan distance measure with/without image stretching. In addition, the experimental results using image preprocessing are also presented. The results indicate that the new measure is unbiased and performs well for histogram matching optimization.