Reducing false positives in Loc8

Finding GCPs in Loc8 was the topic of my previous blog post. I was able to find many of the points but was given many false positives. I believe this was due to using "Min pixel" at 1 and a large range of colors taken from the target. I also mentioned that the samples I took were affected by the distance from camera to target, the colors blurred together a bit. My first step in reducing false positives was to eliminate this issue, and hopefully this would reduce the color range collected.

Figure 1: GCPm2 samples

Figure 1: GCPa2 samples

When comparing the color ranges collected from aerial imagery (figures 3 and 4) to the ranges collected from the ground we can see that the ground collection has more vibrant colors.

Figure 3: GCPm color range

Figure 4: GCPa color range

Since I decided to change up the data sampling method in this test I decided to keep the settings the same, shown in figure 5. This way I could determine the effect of collecting data from images taken prior to the search.

Figure 5: Settings used

During this round of processing Loc8 alerted me to 18 images, 14 false positives and 4 positives. In my opinion this is already a massive improvement over the previous run. While I was still given mostly false positive alerts I was only given 14 of them instead of 132. The 14 false positives each had one or two alerts in each image which reduced the amount of time I had to spend checking each alert.

Figure 6: False positive

  

Figure 7: Found Aeropoint

 Figure 7 shows a successfully located aeropoint, but also shows two manual GCPs that were not found. Figure 8 zooms in closer to allow for a clearer view of all 3 targets.

Figure 8

Figure 9: Found manual GCP

Figure 9 shows a manual GCP being found, but multiple aeropoints being ignored. The "scents" given to the software were able to find both kinds of GCPs, but still generated a high ratio of false positives to positives. While the ratio was still high this method reduced the number of images flagged by false positives by ~90%, and the total number of false positives by more than that. There were also many false negatives given by this method, and that will need to be corrected for. I believe one way to find a balance between the two could be to set a color range based on an image captured before the flight.