Objective

2

Remote sensing building object segmentation is one of the important applications in image processing

which plays a vital role in smart city planning and urban change detection. However

building objects in remote sensing images have many complex characteristics

such as variable sizes

dense distributions

diverse topological shapes

complex backgrounds

and presence of occlusions and shadows. Traditional building segmentation algorithms are mainly based on manually designed features such as shapes

edges

and shadow features. These features are shallow features of the building target and cannot well express high-level semantic information

resulting in low recognition accuracy. By contrast

deep convolutional networks show excellent performance in pixel-level classification of natural images. Various fully convolutional network based image segmentation models have been continuously proposed. Most of these models use deconvolution or bilinear interpolation after feature extraction. Feature upsampling and pixel-by-pixel classification are used to segment the input image. The deep features of the building are extracted using highly nonlinear mapping and a large amount of data training

which overcomes the shortcomings of traditional algorithms. However

upsampling cannot completely compensate the information loss caused by repeated convolution and pooling operations in the deep convolutional network model. Therefore

the prediction results are relatively rough

such as small target misclassification

inaccurate boundaries

and other issues. In the field of remote sensing

public data sets are few. Training excellent deep convolutional networks is difficult

and the robustness of the network needs to be further improved. Aiming at the above problems

this paper proposes a conditional generative adversarial network (Ra-CGAN) with multilevel channel attention mechanism to segment remote sensing building objects.

Method

2

A generative model with a multilevel channel attention mechanism is first built. The model is based on a coding and decoding structure that solves small target misses by fusing deep semantics and shallow details with attention. Second

a discriminative network is built and used to distinguish whether the input comes from the real label map or the segmentation map generated by the model. The segmentation result (accuracy and smoothness) is improved by correcting the difference between the two maps. The downsampling method without pooling is used in the discriminator to enhance the propagation of the gradient. Finally

the generated model and the discriminant model are alternately confronted for training through the constraint of the conditional variable of the labelled image. Learning the higher-order data distribution characteristics results in more continuity for the target space. The loss function uses a hybrid loss function

which comes from the cross-entropy loss function brought by the generated map and the real label map in the generation mode. The discriminator predicts the generated image as the loss value brought by the real label image. Experiments are performed on the WHU Building Dataset and Satellite Dataset II datasets. The first dataset has a dense building with many types and accurate labels

and can provide comprehensive

representative evaluation capabilities for the model. Another dataset with a higher segmentation difficulty is used to verify the robustness and scalability of the model. The lighting information and background information of the building are more complex than those of the first dataset. The experiment uses the PyTorch deep learning framework. The size of original image and the label image are unified to 512×512 pixels for training

the learning rate of Adam is set to 0.000 2

the momentum parameter is 0.5

the batch-size is 12

and the epoch is 200 times. Acceleration is performed using NVIDIA GTX TITAN Xp. Evaluation indicators include intersection over union (IOU)

precision

recall

and F

1

-score.

Result

2

Experiments are performed on the WHU Building Dataset and Satellite Dataset II datasets

and the methods are compared with the latest literature. Experimental results show that in the WHU dataset

the segmentation performance of the Ra-CGAN model is substantially improved compared with models without attention mechanism and adversarial training. Space continuity and integrity of the complex building and small building

and smoothness of building edges are considerably improved. Compared with U-Net

IOU value is increased by 3.75%

and F

1

-score is increased by 2.52%. Compared with the second-performance model

IOU value is increased by 1.1%

and F

1

-score is increased by 1.1%. In the Satellite Dataset II

Ra-CGAN obtains more ideal results in terms of target integrity and smoothness than other models

especially in the case of insufficient data samples. Compared with U-Net

IOU value is increased by 7.26%

and F

1

-score is increased by 6.68%. Compared with the second-placed model

IOU value is increased by 1.7% and F

1

-score is increased by 1.6%.

Conclusion

2

A CGAN remote sensing building object segmentation model with multilevel channel attention mechanism

which combines the advantages of multilevel channel attention mechanism generation model and conditional generative adversarial networks

is proposed. Experimental results show that our model is superior to several state-of-the-art segmentation methods. Much more accurate remote sensing building object segmentation results are obtained on different datasets

proving that the model exhibits better robustness and scalability.