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Flann finds the most similar descriptor in the training set for each descriptor in the query set. If the training set is smaller, then several query descriptors will be matched to the same training descriptor.

You have several possibilities to deal with that: a) use something like RANSAC to select your true inliers b) perform the inverse search (swap training with query) and only select pairs that get assigned to each other in both directions c) decrease the number of matches by using new constraints. e.g. search for the two next neighbours and only create a match if the distance to the first neighbour is less than 60% of the distance to the second neighbour. This will help with repetitive structures in your image

Flann finds the most similar descriptor in the training set for each descriptor in the query set. If the training set is smaller, then several query descriptors will be matched to the same training descriptor.

You have several possibilities to deal with that: that:

a) use something like RANSAC to select your true inliers inliers

b) perform the inverse search (swap training with query) and only select pairs that get assigned to each other in both directions directions

c) decrease the number of matches by using new constraints. e.g. search for the two next neighbours and only create a match if the distance to the first neighbour is less than 60% of the distance to the second neighbour. This will help with repetitive structures in your image