Abstract
In this paper, we propose a novel three-class change detection approach for synthetic aperture radar images (SAR) based on deep learning. In most literatures, change detection in images is a method that classifies the ratio images into two parts: the changed and unchanged classes. However, multitemporal SAR images have either increase or decrease in the backscattering values, so it is significative to further classify the changed areas into the positive and negative changed classes. We accomplish this novel three-class change detection method through Deep Learning. Given the multitemporal images, a difference image which shows difference degrees between corresponding pixels is generated by modified log-ratio operator. Then, we establish a deep belief network to analyze the difference image and recognize the positive changed pixels, negative changed pixels and unchanged pixels.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call