Building Area Estimation in Drone Aerial Images Based on Mask R-CNN

In rural areas where disasters occur frequently, the calculation of building areas is crucial in property assessment. In the segmentation algorithm, Mask R-CNN can distinguish the adjacent objects and extract the outline of an object. Based on this observation, we propose a novel method to calculate...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2021-05, Vol.18 (5), p.891-894
Main Authors: Chen, Jun, Wang, Ganbei, Luo, Linbo, Gong, Wenping, Cheng, Zhan
Format: Article
Language:English
Subjects:
Algorithms
Building area
Buildings
Concept learning
Disasters
drone aerial images
Drone aircraft
Drones
Feature extraction
Image segmentation
Mask R-CNN
Object recognition
Remote sensing
Rural areas
Satellite imagery
Satellites
Task analysis
Training
Transfer learning
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Summary:In rural areas where disasters occur frequently, the calculation of building areas is crucial in property assessment. In the segmentation algorithm, Mask R-CNN can distinguish the adjacent objects and extract the outline of an object. Based on this observation, we propose a novel method to calculate the building areas based on Mask R-CNN and adopt the concept of transfer learning to train our model, which can achieve good results with a small number of drone aerial images as training samples. The proposed method involves three main steps: 1) pretraining using open-source satellite remote sensing images; 2) fine-tuning with a small number of drone aerial images; and 3) testing with new images and area calculation based on the number of building pixels. The experiments show that the proposed method can achieve good results in terms of F1 score and intersection over union.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.2988326