Building Extraction from Remote Sensing Imagery Based on Scale-Adaptive Fully Convolutional Network

Fan Feng; Shuangting Wang; Jin Zhang; Chunyang Wang; Bing Liu

doi:10.3788/LOP202158.2428006

[1] Liu C, Huang X, Zhu Z et al. Automatic extraction of built-up area from ZY3 multi-view satellite imagery: analysis of 45 global cities[J]. Remote Sensing of Environment, 226, 51-73(2019).

[2] Ma L, Liu Y, Zhang X L et al. Deep learning in remote sensing applications: a meta-analysis and review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177(2019).

[3] Li W J, He C H, Fang J R et al. Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source GIS data[J]. Remote Sensing, 11, 403(2019).

[4] Ji S P, Wei S Q, Lu M. A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery[J]. International Journal of Remote Sensing, 40, 3308-3322(2019).

[5] Wu Z H, Gao Y M, Li L et al. Fully convolutional network method of semantic segmentation of class imbalance remote sensing images[J]. Acta Optica Sinica, 39, 0428004(2019).

[6] Yuan Q Q, Shen H F, Li T W et al. Deep learning in environmental remote sensing: achievements and challenges[J]. Remote Sensing of Environment, 241, 111716(2020).

[7] Li Y, Zhang H K, Shen Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 9, 67(2017).

[8] Wang J C, Shen L, Qiao W F et al. Deep feature fusion with integration of residual connection and attention model for classification of VHR remote sensing images[J]. Remote Sensing, 11, 1617(2019).

[9] Feng F, Wang S T, Zhang J et al. Hyperspectral images classification based on multi-feature fusion and hybrid convolutional neural networks[J]. Laser & Optoelectronics Progress, 58, 0810010(2021).

[10] Maggiori E, Tarabalka Y, Charpiat G et al. Convolutional neural networks for large-scale remote-sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 645-657(2017).

[11] Zhang Z X, Wang Y H. JointNet: a common neural network for road and building extraction[J]. Remote Sensing, 11, 696(2019).

[12] Kang W C, Xiang Y M, Wang F et al. EU-Net: an efficient fully convolutional network for building extraction from optical remote sensing images[J]. Remote Sensing, 11, 2813(2019).

[13] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[14] Ji S P, Wei S Q. Building extraction via convolutional neural networks from an open remote sensing building dataset[J]. Acta Geodaetica et Cartographica Sinica, 48, 448-459(2019).

[15] Cui W H, Xiong B Y, Zhang L Y. Multi-scale fully convolutional neural network for building extraction[J]. Acta Geodaetica et Cartographica Sinica, 48, 597-608(2019).

[16] Tian Q L, Qin K, Chen J et al. Building change detection for aerial images based on attention pyramid network[J]. Acta Optica Sinica, 40, 2110002(2020).

[17] Liu Y H, Gross L, Li Z Q et al. Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling[J]. IEEE Access, 7, 128774-128786(2019).

[18] Shao Z F, Tang P H, Wang Z Y et al. BRRNet: a fully convolutional neural network for automatic building extraction from high-resolution remote sensing images[J]. Remote Sensing, 12, 1050(2020).

[19] Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 15, 749-753(2018).

[20] Ibtehaz N, Rahman M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 121, 74-87(2020).

[21] Pleiss G, Chen D L, Huang G et al. Memory-efficient implementation of DenseNets[EB/OL]. (2017-7-21)[2021-01-01]. https://arxiv.org/abs/1707.06990

[22] Feng F, Wang S, Wang C et al. Learning deep hierarchical spatial-spectral features for hyperspectral image classification based on residual 3D-2D CNN[J]. Sensors, 19, 5276(2019).

[23] Veit A, Wilber M J, Belongie S J et al. Residual networks behave like ensembles of relatively shallow networks[C]. //Advances in Neural Information Processing Systems, December 5-10, 2016, Barcelona, Spain. [S.l.: s.n.], 550-558(2016).

[24] Liu Y G, Yu J Z, Han Y H. Understanding the effective receptive field in semantic image segmentation[J]. Multimedia Tools and Applications, 77, 22159-22171(2018).

[25] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6230-6239(2017).

[26] Ji S P, Wei S Q, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 574-586(2019).

[27] Mnih V. Machine learning for aerial image labeling[D], 84-88(2013).

[28] Sun Y, Tian Y, Xu Y P. Problems of encoder-decoder frameworks for high-resolution remote sensing image segmentation: structural stereotype and insufficient learning[J]. Neurocomputing, 330, 297-304(2019).