5 research outputs found
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
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%
Machine Learning Techniques for Knowledge Extraction from Satellite Images: Application to Specific Area Types
When we want to extract knowledge form satellite images, several well-known image classification and analysis techniques can be concatenated or combined to gain a more detailed target understanding. In our case, we concentrated on specific extended target areas such as polar ice-covered surfaces, forests shrouded by fire plumes, flooded areas, and shorelines. These image types can be described by characteristic features and statistical relationships. Here, we demonstrate that both multispectral (optical) as well as SAR (Synthetic Aperture Radar) images can be used for knowledge extraction. The free availability of image data provided by the European Sentinel-1 and Sentinel-2 satellites allowed us to conduct a series of experiments that verified our classification approaches. This could already be verified in our recent work by quantitative quality tests