Fig. 1

The top left shows a zoomed-in area of the Indian Pines dataset with three example patches created during the patching process with the same center pixel class. The top right shows a zoomed-in area of the Kennedy Space Center dataset with five example patches. The bottom right shows the three types of patching (1) Exclusive patching takes a patch of n x n pixels and reduces the size of the dataset by downsizing the patch into one pixel if all classes in the patch match, similar to convolution. (2) Similar to exclusive patching, majority patching will downsize the patch into one pixel based on the most popular class in that patch. Both exclusive and majority patching are not used in our experiments and other works due to the already small number of labeled pixels. However, we include them as they could be used in future datasets which have potentially millions of labeled pixels. (3) Center pixel creates a n x n patch for each pixel that contains all the neighborhood information of that pixel as input into the CNN. Then it classifies the center pixel in each patch. Farmland datasets like Indian Pines have better neighborhood information than a diffused forest and therefore benefit more heavily from center pixel classification. Datasets like Kennedy Space Center have less useful neighborhood information and CPC has little impact on overall test accuracy [11] as shown in Tables 5 and 7. The bottom right shows how the three patches with the same center pixel class from the Indian Pines have identical neighbors, this shows the high value of the neighborhood information, therefore patching would be a useful step to improve semantic segmentation accuracy. However, in contrast, the patches in the Kennedy Space Center dataset do not have similar-looking neighbors and therefore the neighborhood information is not as useful