Semi-automatic Extraction of Rural Roads From High-Resolution Remote Sensing Images Based on a Multifeature Combination

Roads play a vital role in rural economic development. However, rural roads feature irregular curvature changes, are narrow, and are built using diverse construction materials, which renders the analysis of road geometric and spectral characteristics less certain and reduces the automation ability o...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Dai, Jiguang, Ma, Rongchen, Ai, Haibin
Format: Article
Language:English
Subjects:
Analytical models
Automation
Construction materials
Curvature
Data mining
Economic development
Economics
Extraction
Feature extraction
High resolution
Histograms
hue
Image color analysis
Image resolution
Imagery
Methods
Model matching
multiple circular templates
Remote sensing
Resolution
Road construction
Roads
Roads & highways
Rural areas
rural road
Rural roads
Satellite imagery
Saturation
Saturation (color)
Tracking
value (HSV) space
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Summary:Roads play a vital role in rural economic development. However, rural roads feature irregular curvature changes, are narrow, and are built using diverse construction materials, which renders the analysis of road geometric and spectral characteristics less certain and reduces the automation ability of existing methods. Thus, this letter proposes a semiautomatic extraction of rural roads from high-resolution remote sensing images based on a multifeature combination. First, to address irregular curvature change characteristics of rural roads, we modify multiscale line segment orientation histogram (MLSOH) descriptors to reduce the impact of local curvature change on tracking. Second, we design a multicircle template to analyze the contrast between roads and nonroads and solve the problem of narrow roads. Last, we propose a panchromatic and hue, saturation, value (HSV) space interactive matching model to solve the problem of matching diverse road construction materials. This letter employs Pleiades satellite and GF-2 imagery. Compared with other methods, the proposed method improves automated road extraction by ensuring the extraction accuracy.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.3026674