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Adaptive threshold change detection based on type feature for remote sensing image

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：liuhc2016@radi.ac.cn
  • Introduction： 刘红超，1993年生，男，硕士研究生，研究方向为土地覆盖分类及变化检测。E-mail: liuhc2016@radi.ac.cn
LIU Hongchao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhanglei@radi.ac.cn
  • Introduction： 张磊，1965年生，男，研究员，研究方向为土地覆盖遥感监测、土地覆盖制图的分类算法、变化检测、精度验证。E-mail: zhanglei@radi.ac.cn
ZHANG Lei12*

реферат

Change detection with single-phase remote sensing image between two different times is widely used in land cover, urban expansion, coral reef health, forest fire events, and deforestation. The most important step in change detection is to determine the change threshold value, which is used to distinguish change and no-change areas. Traditional change detection methods usually determine only one threshold. These methods neglect the difference of spectral value range between different land cover types. Even the same land cover types may have great differences. For example, areas of farmland that have been harvested are different from areas that have not been harvested. Thus, we propose an adaptive multi-threshold value remote sensing image change detection method that is based on land cover type feature.Land cover data for 2015 and two Landsat 8 OLI images for 2013 and 2015 were collected. First, the method used Temporally Invariant Cluster (TIC) to ensure the consistency of the radiometric level of the two images. To avoid salt-and-pepper noise, we segmented the remote sensing image with multiscale segmentation algorithm. The segmentation spatial scales 200, 150, and 100 were used for different land cover types. Change vectors of the image objects at different segmentation spatial scales were then constructed. The maximum inter-class variance method is used to determine the change detection in single and multi-threshold values that are based on land cover types. Finally, we collected 500 samples by using visual interpretation and subsequently conducted accuracy assessment on the result of single and multi-threshold value change detection.The experiment outcomes showed that the multi-threshold value change detection method had higher accuracy and greater stability than the single threshold value change detection. The total accuracy of the multi threshold value change detection is 87.2%, whereas the total accuracy of the single threshold value change detection is 79.6%. The Kappa coefficient is 0.741 and 0.601, respectively. To compare the proposed multi- threshold values method with the traditional single threshold value method, we conducted further accuracy assessment with each land cover type. Results showed that the producer’s accuracy of the no-change area in farmland, water, as well as developed and barren land was improved. Similarly, the user’s accuracy of the change area in farmland and water was enhanced. The multi-threshold values change detection method weakened the influence of phenology phase to an extent and has better applicability.The TIC relative radiometric normalization method could overcome the shortcomings of traditional visual interpretation for selecting time-invariant pixels. The method avoids the influence of subject factors and can normalize images accurately and efficiently. In addition, the multiscale segmentation algorithm can provide different spatial segmentation scales to avoid over and under segmentation problems. The proposed method involves the change vector analysis driven by different thresholds based on land cover type rather than a single threshold value. The proposed method has improved the accuracy of the change detection and provided a reference for the application of efficiently updating of land cover data in large-scale area.

ключеви́че слова́

remote sensing land cover;change detection;temporally invariant cluster;multiscale segmentation;relative radiometric correction;maximum inter-class variance

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