Building Footprint Extraction Using Deep Learning Semantic Segmentation Techniques: Experiments and Results

Borba, Philipe; Diniz, Felipe de Carvalho; Silva, Nilton Correia da; Bias, Edilson de Souza

doi:10.1109/igarss47720.2021.9553855

Cited by 4 publications

(1 citation statement)

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“…Because traditional building extraction methods for highresolution remote sensing images often rely on low-level features, and the means of describing and representing building features are single and specific, it is challenging to extract the features of different types of buildings (Borba, et al, 2021;Xiao, et al, 2022). In fact, traditional building extraction methods are not universal and cannot meet the needs of most scenes.…”

Section: Introductionmentioning

confidence: 99%

Extraction of building from remote sensing imagery base on multi-attention L-CAFSFM and MFFM

Jin,

Fu,

Nie

et al. 2023

Front. Earth Sci.

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Building extraction from high-resolution remote sensing images is widely used in urban planning, land resource management, and other fields. However, the significant differences between categories in high-resolution images and the impact of imaging, such as atmospheric interference and lighting changes, make it difficult for high-resolution images to identify buildings. Therefore, detecting buildings from high-resolution remote sensing images is still challenging. In order to improve the accuracy of building extraction in high-resolution images, this paper proposes a building extraction method combining a bidirectional feature pyramid, location-channel attention feature serial fusion module (L-CAFSFM), and meticulous feature fusion module (MFFM). Firstly, richer and finer building features are extracted using the ResNeXt101 network and deformable convolution. L-CAFSFM combines feature maps from two adjacent levels and iteratively calculates them from high to low level, and from low to high level, to enhance the model’s feature extraction ability at different scales and levels. Then, MFFM fuses the outputs from the two directions to obtain building features with different orientations and semantics. Finally, a dense conditional random field (Dense CRF) improves the correlation between pixels in the output map. Our method’s precision, F-score, Recall, and IoU(Intersection over Union) on WHU Building datasets are 95.17%、94.83%、94.51% and 90.18%. Experimental results demonstrate that our proposed method has a more accurate effect in extracting building features from high-resolution image.

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Section: Introductionmentioning

confidence: 99%