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Multi-task deep learning architecture for semantic segmentation in EO imagery (Conference Presentation)

Paper Abstract

Semantic segmentation deep learning architectures provide incredible results in segmentation and classification of various scenes. These convolutional-based networks create deep representations for classification and have extended connected weight sets to improve the boundary characteristics of segmentation. We propose a multi-task architecture for these deep learning networks to further improve the boundary characteristics of neural networks. Basic edge detection architectures are able to develop good boundaries, but are unable to fully characterize the necessary boundary information from the imagery. We supplement these deep neural network architectures with specific boundary information to remove boundary features that are not indicative of the boundaries of the classified regions. We utilize various standard semantic segmentation datasets, like Cityscapes and MIT Scene Parsing Benchmark, to test and evaluate the network architectures. When compared to the original architectures, we observe an increase in segmentation accuracy and boundary recreation using this approach. The incorporation of multi-task learning helps improve the semantic segmentation results of the deep learning architectures.

Paper Details

Date Published: 13 May 2019
PDF
Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 1099503 (13 May 2019); doi: 10.1117/12.2520254
Show Author Affiliations
Theus Aspiras, Univ. of Dayton (United States)
Bradley Sorg, Univ. of Dayton (United States)
Vijayan K. Asari, Univ. of Dayton (United States)


Published in SPIE Proceedings Vol. 10995:
Pattern Recognition and Tracking XXX
Mohammad S. Alam, Editor(s)

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