Object-oriented key point vector distance for binary land cover change detection using vhr remote sensing images

Zhiyong Lv*, Tongfei Liu, Jon Atli Benediktsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Very high-resolution (VHR) remote sensing images can geometrically depict ground targets in detail but are usually insufficient in the spectral domain. This characteristic leads to a considerable amount of noise and pseudo change in the produced binary change detection maps (BCDMs) when VHR remote sensing images are used for change detection. Here, to solve the aforementioned problem, an object-oriented key point vector distance (KPVD) is proposed to measure the change magnitude between bitemporal VHR images when land cover changes are detected. The proposed KPVD-based change detection approach comprises the following major steps. First, multiscale objects based on a postevent image are extracted by the fractional net evaluation segmentation approach, and then, the segments are taken as the unit for measuring the change magnitude between bitemporal images. Second, key points and the corresponding vector are defined to describe the object feature instead of using the total pixels within the object. Finally, KPVD is proposed to measure the change magnitude between the local areas referenced to the object in the bitemporal images. The change magnitude image (CMI) between the bitemporal images is generated while the entire images are scanned and processed object by object. A well-known automatic binary method, the Otsu approach, is employed in this article to divide CMI into a BCDM. Experimental results conducted on four real data sets demonstrate the feasibility and outperformance of the proposed KPVD-based change detection approach compared with five state-of-The-Art methods in terms of visual performance and quantitative measurements.

Original languageEnglish
Article number9035661
Pages (from-to)6524-6533
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume58
Issue number9
DOIs
Publication statusPublished - Sept 2020

Bibliographical note

Funding Information:
Manuscript received September 14, 2019; revised December 5, 2019 and January 21, 2020; accepted February 25, 2020. Date of publication March 13, 2020; date of current version August 28, 2020. This work was supported in part by the National Science Foundation China under Grant 61701396 and in part by the Natural Science Foundation of Shaanxi Province under Grant 2017JQ4006. (Corresponding author: Zhiyong Lv.) Zhiyong Lv and Tongfei Liu are with the School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China (e-mail: [email protected]; [email protected]).

Publisher Copyright:
© 1980-2012 IEEE.

Other keywords

  • Key point vector distance (KPVD)
  • land cover change detection (LCCD)
  • multiscale segmentation
  • very high-resolution (VHR) remote sensing image

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