Liu, Z.; Luo, S.; Wang, Y. Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images. Remote Sens.2023, 15, 4558.
Liu, Z.; Luo, S.; Wang, Y. Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images. Remote Sens. 2023, 15, 4558.
Liu, Z.; Luo, S.; Wang, Y. Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images. Remote Sens.2023, 15, 4558.
Liu, Z.; Luo, S.; Wang, Y. Mix MSTAR: A Synthetic Benchmark Dataset for Multi-Class Rotation Vehicle Detection in Large-Scale SAR Images. Remote Sens. 2023, 15, 4558.
Abstract
The application of deep learning in the detection of Synthetic Aperture Radar (SAR) targets has been primarily limited to large objects such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5,392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478x1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate 9 benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex background. Mix MSTAR is available at: https://github.com/TheGreatTreatsby/Mix-MSTAR-mmrotate.
Keywords
SAR vehicle detection; rotated object detection; Synthetic dataset; Mix MSTAR; deep learning
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
