Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation

ABSTRACT

Remote sensing image segmentation plays an important role in many industrial-grade image processing applications. However, the problem of uncertainty caused by intraclass heterogeneity and interclass blurring is prevalent in high-resolution remote sensing images. Moreover, the complexity of information in high-resolution remote sensing images leads to a large amount of background information around objects. To solve this problem, a new fuzzy convolutional neural network is proposed in this paper. This network resolves the ambiguity and uncertainty of feature information by introducing a fuzzy neighbourhood module in the deep learning network structure. In addition, it adds a multi-attention gating module to highlight small object features and separate them from the complex background information to achieve fine segmentation of high-resolution remote sensing images. Experimental results on three different segmentation datasets suggest that the proposed method has higher segmentation accuracy and better performance than other deep learning networks, especially for complicated shadow information. Code will be provided in (https://github.com/tingtingqu/code).

KEYWORDS:

• Fuzzy neighbourhood
• high-resolution remote sensing image
• image segmentation
• multi-attention gating

Acknowledgments

All authors would sincerely thank the reviewers and editors for their suggestions and opinions for improving this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data used to support this work will be provided in the https://github.com/tingtingqu/code.

Additional information

Funding

This research was funded by the National Natural Science Foundation of China under Grant 62072391 and Grant 62066013.