A Feature Difference Convolutional Neural Network-Based Change Detection Method

Research output: Journal article publicationJournal articleAcademic researchpeer-review

174 Citations (Scopus)


Change detection based on remote sensing (RS) images has a wide range of applications in many fields. However, many existing approaches for detecting changes in RS images with complex land covers still have room for improvement. In this article, a high-resolution RS image change detection approach based on a deep feature difference convolutional neural network (CNN) is proposed. This approach uses a CNN to learn the deep features from RS images and then uses transfer learning to compose a two-channel network with shared weight to generate a multiscale and multidepth feature difference map for change detection. The network is trained by a change magnitude guided loss function proposed in this article and needs to train with only a few pixel-level samples to generate change magnitude maps, which can help to remove some of the pseudochanges. Finally, the binary change map can be obtained by a threshold. The approach is tested on several data sets from different sensors, including WorldView-3, QuickBird, and Ziyuan-3. The experimental results show that the proposed approach achieves better performance compared with other classic approaches and has fewer missed detections and false alarms, which proves that the proposed approach has strong robustness and generalization ability.

Original languageEnglish
Article number9052762
Pages (from-to)7232-7246
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number10
Publication statusPublished - Oct 2020
Externally publishedYes


  • Change detection
  • convolutional neural network (CNN)
  • deep feature
  • high spatial resolution
  • remote sensing (RS)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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