Presentation @ WILF 2021

1. Segmentation of remotely sensed images
with a neuro-fuzzy inference system
Giovanna Castellano, Ciro Castiello, Andrea Montemurro, Gennaro Vessio, Gianluca Zaza
Department of Computer Science, University of Bari, Italy
WILF 2021

2. Main motivations
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3. Context
The semantic segmentation of remotely sensed
images is an important task for many applications:
❏ agriculture
❏ biodiversity conservation
❏ urban planning
❏ …
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4. (Scientific) motivations
In recent years, the dominant approach has been
the one based on Deep Learning; however:
❏ huge labeled datasets are typically required
❏ explanations are not provided
An alternative strategy is to use Fuzzy Inference
Systems; however:
❏ acquiring domain knowledge is laborious,
error-prone and highly subjective
Goal: why not integrating both approaches into a unified framework for remote sensing segmentation?
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5. ANFIS (sketch)
Input
features
Fuzzyfication
Product
Normalization
Defuzzyfication
Output
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6. Experimental setting
❏ Wuhan Dense Labeling Dataset
❏ 18 images → 1 179 648 labeled pixels
❏ 6 pixel classes:
❏ vegetation (26%)
❏ building (19%)
❏ pavement (18%)
❏ water (14%)
❏ road (12%)
❏ bare soil (11%)
❏ Features:
❏ RGB components
❏ local entropy
❏ # of fuzzy sets per input feature
❏ 2 → 16 fuzzy rules (NFC1)
❏ 3 → 64 fuzzy rules (NFC2)
❏ 4 → 256 fuzzy rules (NFC3)
❏ # epochs: 200
❏ Mini-batch size: 32
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7. Classification results
Training time & accuracy
Precision & recall
Comparison with SOA
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8. Learned rules
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9. Segmentation
results
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10. Conclusion and future work
The results obtained are encouraging:
❏ an effective model can be learned using a very limited dataset
❏ the rules derived can provide understandable explanations
Future work: the interpretability and relevance of the learned fuzzy rules may be
further investigated using user feedback
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11. Thanks for the
attention!
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