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Dual-thresholds change detection in GF-3 SAR images

  • role： First author第一作者
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

    Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China

  • Email：cuibinwing@126.com
  • Introduction：崔斌, 1989年生，男, 博士研究生, 研究方向为SAR图像处理，变化检测。E-mail: cuibinwing@126.com
CUI Bin123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

  • Email：yhzhang@casm.ac.cn
  • Introduction：张永红，1973年生，男，研究员，研究方向为SAR图像处理，土地利用与覆盖变化。E-mail: yhzhang@casm.ac.cn
ZHANG Yonghong2*,  
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

YAN Li1,  
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

Wei Jujie2

resumen

Compared with the single threshold segment method in SAR change detection, the dual thresholds segment method can simultaneously identify the change areas and confirm the change types. Although D-GKIT shows a superior performance, a strongly overlapping gray level is observed in the histogram of the difference image, thereby inaccurately identifying double thresholds on the same side of the peak. In this paper, we apply a dual-thresholds method combined with normalized maximal between-class variance and GKIT test on GF-3 images to verify its feasibility of our method and the ability of change detection ability.First, the normalized maximal between-class variance values of two sides surrounding the peak in the histogram are taken as the degrees of the overlapping gray level, and then the thresholds selection sequence and the candidate intervals are confirmed. Second, the side at which the gray level lightly overlaps is segmented by GKIT, and the threshold and the fitting function of the unchanged class are obtained. Third, the fitting function of the unchanged class is used to replace the corresponding part in the origin histogram to form a new histogram that is subsequently segmented to obtain the threshold in the second candidate interval. Finally, the two thresholds are applied on the difference image to obtain the final change result.The experiment on GF-3 SAR images reveals that the performance on our proposed method outperforms D-GKIT and can deal well with the overlapping gray level overlapped in the histogram of the difference image. The confusion matrix of the results for various local areas in the change image also shows that the proposed method has been slightly influenced by the overlapping gray level overlapped and obtains generally good results. Therefore, the feasibility of our method and its change detection ability by using GF-3 images are verified.We propose a method based on the normalized maximal between-class variance and GKIT to segment a difference image by applying dual thresholds in SAR change detection. The effectiveness of the proposed method and its change detection ability by using GF-3 images are validated by the experiment results.

palabra clave

remote sensing;SAR change detection;dual thresholds segment;GF-3;GKIT;normalized maximal between-class variance

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