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CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images

Liu, Qi; Li, Yang; Bilal, Muhammad; Liu, Xiaodong; Zhang, Yonghong; Wang, Huihui; Xu, Xiaolong

Authors

Qi Liu

Yang Li

Muhammad Bilal

Yonghong Zhang

Huihui Wang

Xiaolong Xu



Abstract

In recent years, AI and Deep Learning (DL) methods have been widely used for object classification, recognition, and segmentation of high-resolution multispectral remote sensing images. These DL-based solutions perform better compare to traditional spectral algorithms but still suffer from insufficient optimization of global and local features of object context. In addition, failure of code-data isolation and/or disclosure of detailed eigenvalues causes serious privacy and even secret leakage due to the sensitivity of high-resolution remote sensing data and their processing mechanisms. In this paper, Class Feature (CF) modules have been presented in the decoder part of an attention-based CNN network to distinguish between building and non-building (background) area. In this way, context features of a focused object can be extracted with more details being processed, whilst the resolution of images is maintained. The reconstructed local and global feature values and dependencies in the proposed model are maintained by reconfiguring multiple effective attention modules with contextual dependencies to achieve better results for the eigenvalue. According to quantitative results and their visualization, the proposed model has depicted better performance over others' work using two large-scale building remote sensing datasets. The F1-score of this model reached 87.91 and 89.58 on WHU Buildings Dataset and Massachusetts Buildings Dataset, respectively, which exceeded the other semantic segmentation models.

Journal Article Type Article
Acceptance Date Feb 8, 2023
Online Publication Date Feb 13, 2023
Publication Date 2023
Deposit Date Feb 19, 2023
Publicly Available Date Feb 20, 2023
Journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Print ISSN 1939-1404
Electronic ISSN 2151-1535
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 16
Pages 2481-2491
DOI https://doi.org/10.1109/jstars.2023.3244336
Keywords Building extraction, class feature, semantic segmentation

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