Extreme Self-Paced Learning Machine for On-Orbit SAR Images Change Detection

• Main Authors: Shuyuan Yang, Zhi Liu, Quanwei Gao, Yuteng Gao, Zhixi Feng
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Change detection; synthetic aperture radar; extreme self-paced learning machine; affinity propagation super-pixel clustering; manifold regularizer
• Online Access: https://ieeexplore.ieee.org/document/8796343/

With the rapid development of earth observation satellites, on-orbit data processing is becoming more and more desirable. In this paper, a new on-orbit change detection method for Synthetic Aperture Radar (SAR) images, is proposed via an Extreme Self-paced Learning Machine (ESLM). First, a reflectivity-spatial affinity is defined to measure the similarity between two segmented super-pixels, to identify the initial three groups of pixels: strictly changed, strictly unchanged and fuzzy pixels. Then a new extreme self-paced learning machine is developed, by gradually selecting the most confident changed pixels and predicting the changed pixels in an incremental pattern. Moreover, both the labeled and unlabeled samples are explored to realize semi-supervised classification. Different with the available methods, ESLM works in a self-paced learning pattern and achieves accurate detection, for it can automatically choose the training samples and explore unlabeled samples to enhance the online prediction of changes. Therefore, ESLM has the characteristics of accurate and robust detection, parameter free, low-complexity and rapid implementation, which is very suitable for on-orbit processing. Some experiments are taken on five real benchmark datasets, and the results verify the effectiveness of ESLM.