FENet: feature enhancement network for land cover classification

ABSTRACT

Extracting mask information of buildings and water areas from high resolution remote sensing images is beneficial to monitoring and management of urban development. However, due to different times, different geographical locations and different remote sensing acquisition angles, water areas and buildings will feed back different spectral information. Existing semantic segmentation methods do not pay enough attention to channel information, and the feature information extracted by downsampling is relatively abstract, which is easy to cause the loss of some details in high-resolution images under complex scenes, leading to the misjudgement of buildings and waters. To solve the existing problems, feature enhancement network (FENet) for high-resolution remote sensing image segmentation of buildings and water areas is proposed. By paying more attention to the characteristic information of the passage, the probability of misjudgement of buildings and waters can be reduced and their edge contour information can be enhanced. The self-attention feature module proposed in this paper encodes the context information and transmits it to the local features, and establishes the relationship between channels through the channel feature enhancement module to reduce the loss of channel feature information. The feature fusion module fuses feature information of different scales in space and outputs more detailed prediction images. Comparative experiments show that this model is superior to the existing classical semantic segmentation model. Compared with the existing models, the proposed method can achieve 2% improvement than PSPNet on the indicator MIoU, and the final MIoU reaches 82.85% for land cover dataset. This study demonstrates the advantages of our proposed method in land cover classification and detection.

KEYWORDS:

• Semantic segmentation
• Land cover segmentation
• Feature enhancement
• Deep learning
• High-resolution remote sensing image

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 42075130.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Computer code availability

Name of code: FENet.py

Developer: Zhanming Ma etc.,email: [email protected]

Year first available: 2021

CPU: i7–10700, RAM 16 G

Experimental software: Python 3.6

Packages: PyTorch

The trained networks are available at https://github.com/BINBINFORK/FENet

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42075130]