End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

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, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an…

• NN
  National Natural Science Foundation of China

  Awards: 41801386, 41801386 41671454, 41671454, 41801386 41671454
• GO
  Government of Jiangsu Province
• NS
  Natural Science Foundation of Jiangsu Province

  Award: BK20180797
• NU
  Nanjing University of Information Science and Technology

  Award: 2018r029