Generating Negative Examples

To improve the performance of the classifier, we generate negative examples. The following strategy takes negative examples from the eight non-overlapping windows surrounding the positive example, and equal to the positive example's dimensions. The result is shown below. Positive examples are shown in yellow, and suggested negatives in red:

An LRO image with suggested negative examples.

An A15 image with suggested negative examples.

The advantages of this strategy are:

• 8-1 negative to positive example ratio
• Less computationally expensive than random or full sliding widow
• All sizes of examples
• Learn the classifier not to mistake the surroundings of a crater for a crater

Disadvantages:

• May overfit due to training negative examples only on the sizes of positive training examples
• Negative examples sometimes coincide with positive ones (this can be fixed)

Another strategy: Slide a window over the image in small increments and label all that don't coincide greatly with positive examples as negative.