Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images

Zhiyong Lv, Feng Jun Wang, Tongfei Liu, Xiang Bin Kong, Jon Atli Benediktsson

Research output: Contribution to journalArticlepeer-review


Many land cover change detection (LCCD) approaches applied on very high resolution (VHR) remote sensing images utilize spatial information by using a regular window or strict mathematical model. However, regular shape or strict models cannot fit the various shapes and sizes of the ground targets. In this article, a novel LCCD approach without the parameter is proposed to detect land cover change with VHR remote sensing images. First, an adaptive spatial-context extraction algorithm is applied to explore contextual information around a pixel. Second, the change magnitude between pairwise pixels is quantitatively measured by computing the band-to-band distance which is defined by the pairwise adaptive regions around the corresponding pixels. Finally, after the generation of a change magnitude image (CMI), a binary threshold method called double-window flexible pace search (DFPS) is adopted to divide CMI into a binary change detection map. The performance of the proposed approach is verified by comparing it with five state-of-the-art methods with three pairs of VHR images. The comparisons demonstrated that the proposed approach achieved the improved detected results comparing with state-of-the-art LCCD methods. The code of the proposed approach is available at

Original languageEnglish
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - 1 Jan 2021

Bibliographical note

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Other keywords

  • Adaptive region extension algorithm
  • land cover change detection (LCCD)
  • Mathematical model
  • Remote sensing
  • Shape
  • spatial context extraction
  • Spatial resolution
  • Training
  • very high-resolution remote sensing images.
  • Visualization
  • Windows


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