Classification of Large-Scale High-Resolution SAR Images with Deep
Transfer Learning
Abstract
The classification of large-scale high-resolution SAR land cover images
acquired by satellites is a challenging task, facing several
difficulties such as semantic annotation with expertise, changing data
characteristics due to varying imaging parameters or regional target
area differences, and complex scattering mechanisms being different from
optical imaging. Given a large-scale SAR land cover dataset collected
from TerraSAR-X images with a hierarchical three-level annotation of 150
categories and comprising more than 100,000 patches, three main
challenges in automatically interpreting SAR images of highly imbalanced
classes, geographic diversity, and label noise are addressed. In this
letter, a deep transfer learning method is proposed based on a similarly
annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2
smooth loss function with cost-sensitive parameters was introduced to
tackle the label noise and imbalanced classes’ problems. The proposed
method shows high efficiency in transferring information from a
similarly annotated remote sensing dataset, a robust performance on
highly imbalanced classes, and is alleviating the over-fitting problem
caused by label noise. What’s more, the learned deep model has a good
generalization for other SAR-specific tasks, such as MSTAR target
recognition with a state-of-the-art classification accuracy of 99.46%.