decreasing spatial resolution) for all of the three mass movement examples investigated. The effect youre seeing makes perfect sense. Note that this isnt a 'bug' in the normalized cross correlation. Or a 'flat' area in the search image, either. So in a nutshell: You cant match a 'flat' template using normalized cross-correlation. The study also quantifies how the mean error, the random error, the proportion of mismatches and the proportion of undetected movements increase with increasing pixel size (i.e. Multiply this by 0 and add 91 - and you have a perfect match. reducing the ground pixel size) of the matched images by 2 to 16 times using intensity interpolation, 40% to 80% reduction in mean error in reference to the same resolution original image could be achieved. By increasing the spatial resolution (i.e. Both Gaussian and parabolic peak locating turn out less accurate. Normalized correlation is considered one of the methods based on template matching that can be used for finding a presence of a pattern or a feature within an image. Our results show that bi-cubic interpolation of image intensity performs best followed by bi-cubic interpolation of the correlation surface. In addition, the influence of pixel resolution on the accuracies of displacement measurement using image matching is evaluated using repeat images resampled to different spatial resolutions. Both principal approaches are applied to three typical mass movement types: rockglacier creep, glacier flow and land sliding. Normalized cross-correlation (NCC) is the main matching algorithm for template matching method. In the second approach, the image pairs are correlated at the original image resolution and the peaks of the correlation coefficient surface are then located at the desired sub-pixel resolution using three techniques, namely bi-cubic interpolation, parabola fitting and Gaussian fitting. Template matching is one of the best and the most widely used pattern recognition method. In the first approach, image intensities are interpolated to a desired sub-pixel resolution using a bi-cubic interpolation scheme prior to the actual displacement matching. Total running time of the script: ( 0 minutes 0.This study evaluates the performance of two fundamentally different approaches to achieve sub-pixel precision of normalised cross-correlation when measuring surface displacements on mass movements from repeat optical images. plot ( x, y, 'o', markeredgecolor = 'r', markerfacecolor = 'none', markersize = 10 ) plt. set_title ( '`match_template` \n result' ) # highlight matched region ax3. Rectangle (( x, y ), wcoin, hcoin, edgecolor = 'r', facecolor = 'none' ) ax2. If you check the formula below you can see that denumerator for B(x)template will be much bigger than A(x)template. set_title ( 'image' ) # highlight matched region hcoin, wcoin = coin. Here it is clear that A is the same as template but correlation between B and template is bigger than A and template.In normalized cross correlation denumerator part of formula is solving this problem. subplot ( 1, 3, 3, sharex = ax2, sharey = ax2 ) ax1. coins () coin = image result = match_template ( image, coin ) ij = np. Or in other words, when matching low-resolution images using normalised cross-correlation with intensity-interpolation based sub-pixel precision, 40 or better accuracy increment can be achieved compared to pixel-precision matching in reference to images with the same original resolution as the interpolated image. Import numpy as np import matplotlib.pyplot as plt from skimage import data from skimage.feature import match_template image = data.