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A Deep Neural Network for Image Segmentation

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DOI: 10.23977/jeis.2023.080602 | Downloads: 18 | Views: 209

Author(s)

Fan Yu 1, Haoran Gui 1, Huawei Wan 2

Affiliation(s)

1 Beijing University of Civil Engineering and Architecture (BUCEA), Beijing, China
2 Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing, China

Corresponding Author

Fan Yu

ABSTRACT

Many tasks demand high-quality remote sensing image annotation products that are difficult to achieve through existing automated methods. Obtaining high-quality pixel annotations is time-consuming and laborious. This study proposes architecture with controllable correction ability that can automatically generate image annotations and allow annotators to adaptively correct previous annotations by making simple guidance information after discovering errors. This method can be applied to any convolution-based network. A training method and metric were proposed to measure the efficiency of re-annotation. We conducted experiments on the Vaihingen dataset using different base architectures and backbones. Our study shows that our training method can effectively direct the guidance module to utilize the guidance information and improve the re-annotation efficiency up to 2.53 times. In addition, more advanced architectures may give better results.

KEYWORDS

Image Annotation, Machine Learning, Re-annotating

CITE THIS PAPER

Fan Yu, Haoran Gui, Huawei Wan, A Deep Neural Network for Image Segmentation. Journal of Electronics and Information Science (2023) Vol. 8: 7-14. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080602.

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