Reducing Effects of Swath Gaps on Unsupervised Machine Learning Models
for NASA MODIS Instruments
Due to the nature of their pathways, NASA Terra and NASA Aqua satellites
capture imagery containing “swath gaps” which are areas of no data.
Swath gaps can overlap the region of interest (ROI) completely, often
rendering the entire imagery unusable by Machine Learning (ML) models.
This problem is further exacerbated when the ROI rarely occurs (e.g. a
hurricane) and, on occurrence, is partially overlapped with a swath gap.
With annotated data as supervision, a model can learn to differentiate
between the area of focus and the swath gap. However, annotation is
expensive and currently the vast majority of existing data is
unannotated. Hence, we propose an augmentation technique that
considerably removes the existence of swath gaps in order to allow CNNs
to focus on the ROI, and thus successfully use data with swath gaps for
training. We experiment on the UC Merced Land Use Dataset, where we add
swath gaps through empty polygons (up to 20% areas) and then apply
augmentation techniques to fill the swath gaps. We compare the model
trained with our augmentation techniques on the swath gap-filled data
with the model trained on the original swath gap-less data and note
highly augmented performance. Additionally, we perform a qualitative
analysis using activation maps that visualizes the effectiveness of our
trained network in not paying attention to the swath gaps. We also
evaluate our results with a human baseline and show that, in certain
cases, the filled swath gaps look so realistic that even a human
evaluator did not distinguish between original satellite images and
swath gap-filled images. Since this method is aimed at unlabeled data,
it is widely generalizable and impactful for large scale unannotated
datasets from various space data domains.