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Evaluating the Capabilities of DEEPLABV3+ Encoder-Decoder Network with Modified Atrous Convolutions (Case Study: Deep Semantic Building Segmentation)
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Mohammad Erfan Omati   , Fatemeh Tabib Mahmoudi * |
| Shahid Rajaee Teacher Training University |
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Abstract: (1648 Views) |
Building segmentation is a difficult task due to the need for rich semantic features. Differences in the shape, color and size of buildings and their proximity to other features such as parking lots and streets make their recognition in high resolution images challenging. In this research, with the aim of extracting buildings from high-resolution images, deep convolutional neural network architecture of the encoder-decoder type based on the modified DeepLabV3+ model has been used. In the Atrous module of this modified model, convolution layers are applied with lower rates compared to the original module, in order to achieve the goal of performing a more powerful semantic segmentation of small and large building objects. The performance of the proposed model in this research was evaluated using two data sets, WHU and INRIA, and the results showed that using lower Atrous rates and changing them to 4, 8, and 12 significantly improved the segmentation performance in both data sets. The proposed modified model was able to improve the IOU and F-Score indices compared with other advanced models in the WHU data set by 0.39 and 0.53, respectively. In addition, the modified method in the INRIA dataset improved both of the above indices by 0.35. The proposed model in this research, based on the reduction of Atros rates to 4, 8 and 12 and the change in ResNet-50 layers, was able to achieve an IOU equal to 89.51 in the WHU dataset and 76.64 in the INRIA dataset in the extraction of construction charges.
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| Keywords: Semantic Segmentation, Deep Convolutional Neural Network, Encoder-Decoder, Atrous Convolution |
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Full-Text [PDF 1432 kb]
(345 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2023/07/13 | Accepted: 2023/10/3 | Published: 2023/12/21
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