A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images
A novel runway detection algorithm for PolSAR (Polarimetric Synthetic Aperture Radar) images based on optimized polarimetric features and local spatial information is proposed. Existing methods for runway detection for PolSAR images always utilize the parallel line as the primary feature. However, m...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9031331/ |
| _version_ | 1818624274823380992 |
|---|---|
| author | Zhe Zhang Can Zou Ping Han Xiaoguang Lu |
| author_facet | Zhe Zhang Can Zou Ping Han Xiaoguang Lu |
| author_sort | Zhe Zhang |
| collection | DOAJ |
| description | A novel runway detection algorithm for PolSAR (Polarimetric Synthetic Aperture Radar) images based on optimized polarimetric features and local spatial information is proposed. Existing methods for runway detection for PolSAR images always utilize the parallel line as the primary feature. However, many other ground objects such as rivers and roads also have parallel structures thus affect the performance of these detection methods. The proposed method is based on two stages of classification with polarimetric features and the HOG (Histogram of Oriented Gradient) feature, while avoiding the interference due to the similar morphological features among different ground objects. An FCBF (Fast Correlation Based Filter) is firstly used for optimizing and selecting of the ground objects' polarimetric features of ground targets. Then RF (Random Forest) classifier is employed for extracting ROIs (Region of Interest) which may contain runways. Then HOG features are extracted from these ROIs for further classification with SVM (Support Vector Machines) to detect the runway area. Experimental results with the measured PolSAR data provided by NASA UAVSAR project show that the proposed method can detect runway regions effectively without using the parallel line. Comparative analysis is also conducted on parallel line pattern based algorithms. And the results suggest the effectiveness and performance enhancement of this method. |
| first_indexed | 2024-12-16T18:54:22Z |
| format | Article |
| id | doaj.art-79d905ccc92c424eb2a27bfbbbac0ac0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:54:22Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-79d905ccc92c424eb2a27bfbbbac0ac02022-12-21T22:20:35ZengIEEEIEEE Access2169-35362020-01-018491604916810.1109/ACCESS.2020.29797379031331A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR ImagesZhe Zhang0https://orcid.org/0000-0003-4588-5415Can Zou1https://orcid.org/0000-0001-5836-3929Ping Han2https://orcid.org/0000-0001-9633-6286Xiaoguang Lu3https://orcid.org/0000-0003-4702-589XTianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaTianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin, ChinaA novel runway detection algorithm for PolSAR (Polarimetric Synthetic Aperture Radar) images based on optimized polarimetric features and local spatial information is proposed. Existing methods for runway detection for PolSAR images always utilize the parallel line as the primary feature. However, many other ground objects such as rivers and roads also have parallel structures thus affect the performance of these detection methods. The proposed method is based on two stages of classification with polarimetric features and the HOG (Histogram of Oriented Gradient) feature, while avoiding the interference due to the similar morphological features among different ground objects. An FCBF (Fast Correlation Based Filter) is firstly used for optimizing and selecting of the ground objects' polarimetric features of ground targets. Then RF (Random Forest) classifier is employed for extracting ROIs (Region of Interest) which may contain runways. Then HOG features are extracted from these ROIs for further classification with SVM (Support Vector Machines) to detect the runway area. Experimental results with the measured PolSAR data provided by NASA UAVSAR project show that the proposed method can detect runway regions effectively without using the parallel line. Comparative analysis is also conducted on parallel line pattern based algorithms. And the results suggest the effectiveness and performance enhancement of this method.https://ieeexplore.ieee.org/document/9031331/PolSARrunway detectionrandom forestHOGpolarimetric features |
| spellingShingle | Zhe Zhang Can Zou Ping Han Xiaoguang Lu A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images IEEE Access PolSAR runway detection random forest HOG polarimetric features |
| title | A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images |
| title_full | A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images |
| title_fullStr | A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images |
| title_full_unstemmed | A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images |
| title_short | A Runway Detection Method Based on Classification Using Optimized Polarimetric Features and HOG Features for PolSAR Images |
| title_sort | runway detection method based on classification using optimized polarimetric features and hog features for polsar images |
| topic | PolSAR runway detection random forest HOG polarimetric features |
| url | https://ieeexplore.ieee.org/document/9031331/ |
| work_keys_str_mv | AT zhezhang arunwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT canzou arunwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT pinghan arunwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT xiaoguanglu arunwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT zhezhang runwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT canzou runwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT pinghan runwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages AT xiaoguanglu runwaydetectionmethodbasedonclassificationusingoptimizedpolarimetricfeaturesandhogfeaturesforpolsarimages |