Figure 1: GCP without false positives
My last Loc8 post focused on reducing false positives while searching for GCPs in a field. That post described the methods in which I reduced the number of incorrectly identified images in a data-set. One problem I described at the end of the post was a large amount of false positives in correctly identified images, like the image shown below.
Figure 2: Correct Identification with false positives
One of the circles in figure 2 has correctly circled one of the pink GCPs, the bottom rightmost circle, and because of this I marked it as correctly identifying the target. While this works for assessing images in a "non-time-critical" environment, it would be much less useful when time is of the essence. While doing this processing I inspected every pixel that was identified, it became monotonous quickly and I found that I was looking through the hits far too quickly. This rapid searching often had me going back through the image to take another look at a point, just to be sure I had correctly identified it as a positive or false positive hit. Since I have worked with this data-set extensively I know where the GCPs are in most images, and the searching still greatly increased the time I spent inspecting each image.
After many attempts to eliminate/reduce false positives in the data-set I had a conversation with Loc8 and was given the idea to create multiple discrete colors and search that. In my last post I created a spectral range using multiple individual colors, and that did reduce the false positives. This time I tried using multiple "ranges" of individual colors, and received 0 false positives. Figure 3 below is an example from this run. For this test I only focused on the pink GCPs.
Figure 3: No false positives
Figure 3 is one of the 15 images that Loc8 flagged as containing a GCP, and none of them contained a false positive. While figure 3 does not show a false positive it does have two false negatives, the GCPs at the top and bottom of the image have not been identified. There are also many images containing GCPs that were not identified. Finding a balance between false positives and false negatives is the next hurdle to clear.
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