.png){kind=icon}
Return
Using Low-Resolution SAR Scattering Features for Ship Classification
.png){kind=icon}
OA
AI
DOI：10.1109/LGRS.2022.3183622.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This letter reports an experimental study aimed at establishing the questionable usefulness of scattering attributes for ship classification from moderate-resolution synthetic aperture radar (SAR) images. About 2700 example images representing four ship types have been extracted from the OpenSARShip annotated dataset and used to form the training and test sets for random forest models. After importance ranking and cross-validation, different subsets of both geometric and scattering attributes were selected from a fixed training set and used to train the classifier. The results from the validation using the test sets show that the scattering attributes give a significant contribution in terms of overall classification accuracy.
Keywords:
Scattering
Marine vehicles
Feature extraction
Training
Random forests
Meters
Data mining
Low-resolution synthetic aperture radar (SAR) ship classification
random forests
scattering attributes
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K
Organization
Cited Papers
ShareSaveShip Classification in High-Resolution SAR Images via Transfer Learning with Small Training Dataset.png)
SENSORS
OAAI
SENSORSIF3.5
OAAIShareSaveA Maximum-Information-Minimum-Redundancy-Based Feature Fusion Framework for Ship Classification in Moderate-Resolution SAR ImageSENSORSOAAI
SENSORSIF3.5
OAAIShareSaveEnhanced Energy Storage Performance in Mn-Doped SrBi5Ti4FeO18 Thin Films via Defect Engineering
PREAI
ShareSaveShip Classification in High-Resolution SAR Images Using Deep Learning of Small DatasetsSENSORSOAAI
SENSORSIF3.5
OAAIShareSave
View more