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SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
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Remote Sens. 2020, 12(6), 1049; https://doi.org/10.3390/rs12061049
Received: 4 February 2020 / Revised: 6 March 2020 / Accepted: 23 March 2020 / Published: 24 March 2020
The lack of pixel-level labeling limits the practicality of deep learning-based building semantic segmentation. Weakly supervised semantic segmentation based on image-level labeling results in incomplete object regions and missing boundary information. This paper proposes a weakly supervised semantic segmentation method for building detection. The proposed method takes the image-level label as supervision information in a classification network that combines superpixel pooling and multi-scale feature fusion structures. The main advantage of the proposed strategy is its ability to improve the intactness and boundary accuracy of a detected building. Our method achieves impressive results on two 2D semantic labeling datasets, which outperform some competing weakly supervised methods and are close to the result of the fully supervised method. View Full-Text
Keywords: building detection; weakly supervised learning; superpixel; semantic segmentation; deep learning building detection; weakly supervised learning; superpixel; semantic segmentation; deep learning
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Chen, J.; He, F.; Zhang, Y.; Sun, G.; Deng, M. SPMF-Net: Weakly Supervised Building Segmentation by Combining Superpixel Pooling and Multi-Scale Feature Fusion. Remote Sens. 2020, 12, 1049.

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