TY - JOUR

T1 - CFNet

T2 - An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images

AU - Liu, Qi

AU - Li, Yang

AU - Bilal, Muhammad

AU - Liu, Xiaodong

AU - Zhang, Yonghong

AU - Wang, Huihui

AU - Xu, Xiaolong

AU - Lu, Hui

PY - 2023/3/10

Y1 - 2023/3/10

N2 - 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 compared with the 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 cause serious privacy and even secret leakage due to the sensitivity of high-resolution remote sensing data and their processing mechanisms. In this article, class feature modules have been presented in the decoder part of an attention-based CNN network to distinguish between building and nonbuilding (background) area. In this way, context features of a focused object can be extracted with more details being processed while 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.

AB - 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 compared with the 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 cause serious privacy and even secret leakage due to the sensitivity of high-resolution remote sensing data and their processing mechanisms. In this article, class feature modules have been presented in the decoder part of an attention-based CNN network to distinguish between building and nonbuilding (background) area. In this way, context features of a focused object can be extracted with more details being processed while 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.

KW - Building extraction

KW - class feature (CF)

KW - semantic segmentation

U2 - 10.1109/JSTARS.2023.3244336

DO - 10.1109/JSTARS.2023.3244336

M3 - Journal article

AN - SCOPUS:85149399438

VL - 16

SP - 2481

EP - 2491

JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

SN - 1939-1404

ER -