MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
Source abstract
Abstract
The use of deep learning methods to extract buildings from remote sensing images is a key contemporary research focus, and traditional deep convolutional networks continue to exhibit limitations in this regard. This study introduces a novel multi-feature fusion network (MFFNet), with the aim of enhancing the accuracy of building extraction from high-resolution remote sensing images of various sources. MFFNet improves feature capture for building targets by integrating deep semantic information from various attention mechanisms with multi-scale spatial information from a spatial pyramid module, significantly enhancing the results of building extraction. The performance of MFFNet was tested on three datasets: the self-constructed Jilin-1 building dataset, the Massachusetts building dataset, and the WHU building dataset. Notably, experimental results from the Jilin-1 building dataset demonstrated that MFFNet achieved an average intersection over union (MIoU) of 89.69%, an accuracy of 97.05%, a recall rate of 94.25%, a precision of 94.66%, and an F1 score of 94.82%. Comparisons with the other two public datasets also showed MFFNet’s significant advantages over traditional deep convolutional networks. These results confirm the superiority of MFFNet in extracting buildings from different high-resolution remote sensing data compared to other network models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Automated Road and Building Extraction · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
China Coal Aerial Survey and Remote Sensing Group, No. 216, Shenzhou 4th Road, Chang’an District, Xi’an 710100, China
Funding
Priority Academic Program Development of Jiangsu Higher Education Institution
References (36)
Show source-provided references
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