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CFENet

A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. Firstly, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building and the Massachusetts Building Dataset, the efficiency and accuracy of our CFENet balance on all five evaluation metrics: PA, Recall, F1, IoU, and FWIoU. Moreover, our model is smaller, which means it occupies less memory and runs faster. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy.

Our main contributions include the following:

• An end-to-end context feature enhancement network, namely CFENet, is proposed to address the challenges of complexity and diversity of buildings encountered in building extraction from remote sensing images.
• CFENet achieves more accurate building extraction results on the WHU Building Dataset and the Massachusetts Building Dataset by explicitly establishing rich contextual relationships on low-level and high-level features.
• CFENet balances efficiency and accuracy by employing dilated convolution in the spatial fusion module and asymmetric convolution in the focus enhancement module.

Requirements:

An Pytorch implementation of our work.

• pytorch 1.7.0
• CUDA 11.0
• python 3.7

Overview code directory:

${ROOT}/
├── dataset/
├── network/ :contains model definition.
├── pretrained_model/ :includes training model.
├── train_model/ :includes training model.
├── utils/ :contains some utility functions.
├── train.py/ : training scripts for building extraction.
├── metric.py
├── README.md

Implementation details:

Our proposed method is implemented based on pytorch 1.7.0 and cuda 11.0. An Adam optimizer is applied to train our network with a learning rate initialized to 0.0001, and then decayed to 0.1 of the current learning rate every 50 epochs. Our network is trained on the GPU for a number of 200 epochs.

Our CFENet model：

You can download the trained model to verify our results.

• Our train_model (CFENet)
  [CSNetbuilding_extraction.pth]
• The pretrained_model
  [resnet101-5be5422a.pth]

Dataset:

• WHU Building Dataset: download from this link
  [gpcv.whu.edu.cn/data/building_dataset.html]

• Massachusetts Building Dataset: download from this link
  [Road and Building Detection Datasets (toronto.edu)]

References:

• [1] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv:1511.07122, 2015.

• [2] Denton E L, Zaremba W, Bruna J, et al. Exploiting linear structure within convolutional networks for efficient evaluation[J]. Advances in neural information processing systems, 2014, 27.

• [3] Ji S, Wei S, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(1): 574-586.

• [4] Mnih V. Machine learning for aerial image labeling[M]. University of Toronto (Canada), 2013.