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Building-a-nets: robust building extraction from high-resolution remote sensing images with adversarial networks

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lixiang-ucas/Building-A-Nets

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Description

This branch implements a building extraction on remote sensing images, combining the adversarial networks with a FC-DenseNet model.

Network structure

image Overview of our segmentation architecture with the adversarial network. Left: The segmentation network takes an aerial image as input and produces a pixel-wise classification label map. Right: A label map, chosen from segmentation output or ground truth, is multiplied with their corresponding input aerial image to produce a masked image, and the adversarial network takes this masked image map as input and adopts an auto-encoder network to reconstruct it.

Preparation

Train

nohup /home/mmvc/anaconda2/envs/Xiang_Li3/bin/python inria3.py --exp_id 1 --model 'FC-DenseNet158' > log1.log

Test

step 1: to get the prediction results python run_prediction.py

step 1: evaluation python eval_aerial.py prediction_#108

Results

Test accuracy of different models on the Massachuttes dataset.

Model Breakeven ($\rho$ = 3) Breakeven ($\rho$ = 0) Time (s)
Mnih-CNN~\cite{mnih2013machine} 92.71 76.61 8.7
Mnih-CNN+CRF~\cite{mnih2013machine} 92.82 76.38 26.6
Saito-multi-MA~\cite{saito2016multiple} 95.03 78.73 67.7
Saito-multi-MACIS~\cite{saito2016multiple} 95.09 78.72 67.8
HF-FCN~\cite{zuo2016hf} 96.43 84.24 1.07
Ours (56 layers) 96.40 83.17 1.01
Ours (158 layers) 96.78 84.79 4.38

Validation accuracy of different network depths on Inria Aerial Image Labeling dataset.

FC-DenseNet (56 layers) 74.64 96.01
Ours (56 layers) 74.75 96.01
FC-DenseNet (103 layers) 75.58 96.19
Ours (103 layers) 76.31 96.32
FC-DenseNet (158 layers) 77.11 96.45
Ours (158 layers) 78.73 96.71

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Building-a-nets: robust building extraction from high-resolution remote sensing images with adversarial networks

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