A Novel Airport Detection Method via Line Segment Classification and Texture Classification

Airports are one of the most important traffic facilities; thus, airport detection is of great significance in economic and military construction. This letter proposes a novel method for airport detection, with the entire algorithm based on line segment classification and texture classification. Fir...

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Vydáno v:IEEE geoscience and remote sensing letters Ročník 12; číslo 12; s. 2408 - 2412
Hlavní autoři: Tang, Gefu, Xiao, Zhifeng, Liu, Qing, Liu, Hua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.12.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Airport construction
Airport detection
Airport runways
Airports
Classification
Economics
Feature extraction
Fuzzy logic
Image segmentation
line segment classification
line segment detector (LSD)
Neon
region of interest (ROI)
Remote sensing
Segments
support vector machine (SVM)
Support vector machines
Surface layer
Testing
Texture
texture classification
Training
line segment classification
line segment detector (LSD)
region of interest (ROI)
texture classification
Airport detection
support vector machine (SVM)
ISSN:1545-598X, 1558-0571
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Popis
Shrnutí:Airports are one of the most important traffic facilities; thus, airport detection is of great significance in economic and military construction. This letter proposes a novel method for airport detection, with the entire algorithm based on line segment classification and texture classification. First, a fast line segment detector is applied to extract the line segments in images and compute the features of these line segments. Then, the line segments are discriminated by a trained runway line classifier, and the regions of interest (ROIs) are extracted from the line segments, which are classified as runway lines. Finally, whether the ROI is actually an airport is determined by analyzing the classification results of the image blocks. This method is unique in terms of the computing of line segment features and line segment classification. Experimental results demonstrate the effectiveness and robustness of the proposed method.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2015.2479681