Despite recent advances in deep-learning based semantic segmentation, automatic building detection from remotely sensed imagery is still a challenging problem owing to large
variability in the appearance of buildings across the globe. The errors occur mostly around
the boundaries of the building footprints, in shadow areas, and when detecting buildings
whose exterior surfaces have reflectivity properties that are very similar to those of the surrounding regions. To overcome these problems, we propose a generative adversarial network
based segmentation framework with uncertainty attention unit and refinement module
embedded in the generator. The refinement module, composed of edge and reverse attention
units, is designed to refine the predicted building map. The edge attention enhances the
boundary features to estimate building boundaries with greater precision, and the reverse
attention allows the network to explore the features missing in the previously estimated
regions. The uncertainty attention unit assists the network in resolving uncertainties in
classification. As a measure of the power of our approach, as of January 5, 2022, it ranks
at the second place on DeepGlobe’s public leaderboard despite the fact that main focus of
our approach — refinement of the building edges — does not align exactly with the metrics
used for leaderboard rankings. Our overall F1-score on DeepGlobe’s challenging dataset is
0.745. We also report improvements on the previous-best results for the challenging INRIA
Validation Dataset for which our network achieves an overall IoU of 81.28% and an overall
accuracy of 97.03%. Along the same lines, for the official INRIA Test Dataset, our network
scores 77.86% and 96.41% in overall IoU and accuracy. We have also improved upon the
previous best results on two other datasets: For the WHU Building Dataset, our network
achieves 92.27% IoU, 96.73% precision, 95.24% recall and 95.98% F1-score. And, finally, for
the Massachusetts Buildings Dataset, our network achieves 96.19% relaxed IoU score and
98.03% relaxed F1-score over the previous best scores of 91.55% and 96.78% respectively,
and in terms of non-relaxed F1 and IoU scores, our network outperforms the previous best
scores by 2.77% and 3.89% respectively.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/19322168 |
| Date | 18 April 2022 |
| Creators | Somrita Chattopadhyay (12210671) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/UNCERTAINTY_EDGE_AND_REVERSE-ATTENTION_GUIDED_GENERATIVE_ADVERSARIAL_NETWORK_FOR_AUTOMATIC_BUILDING_DETECTION_IN_REMOTELY_SENSED_IMAGES/19322168 |
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