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End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
2019
Remote Sensing
Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches,
doi:10.3390/rs11111382
fatcat:jmdu2ygmqfhjdalfzg63viztey