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Automatic sampling of remote sensing image change detection samples based on prior information of vector data

  • role： First author第一作者
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

    College of Civil Engineering, Central South University of Forestry and Technology, Changsha 410004, China

    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China

    Key Laboratory of Non-ferrous Resources and Geological Hazard Detection, Changsha 410083, China

  • Email：wds@csuft.edu.cn
  • Introduction：魏东升，1979年生，男，讲师，研究方向为地理国情时空变化检测、遥感影像处理。E-mail：wds@csuft.edu.cn
Dongsheng WEI1234,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China

    Key Laboratory of Non-ferrous Resources and Geological Hazard Detection, Changsha 410083, China

  • Email：zxgcsu@foxmail.com
  • Introduction：周晓光，1969年生，女，教授，研究方向为空间数据更新与众源数据处理。E-mail：zxgcsu@foxmail.com
Xiaoguang ZHOU134*

resumen

Some existing classification vector datasets are available for change detection in many regions, and some prior knowledge—such as position, shape, size, and class—are included in these datasets. The change detection method, which rely on remote sensing images and vector datasets, has become an important research focus. The automatic sampling method for the change detection of samples is the key technology in achieving automatic change detection. Therefore, an automatic sampling method based on the feature space density outlier index is proposed. The change detection of vector data/remote sensing images is realized by focusing on the spectral feature difference between the image objects needed to be detected and the samples. On the one hand, the samples must be able to differentiate the regional environment characteristics in a spatial distribution, and this aspect belongs to the problem realm of the sampling distribution method. On the other hand, the samples must include the image objects with no changes in the posterior class attribute, and this aspect is important in the detection of outlier samples. The automatic sampling method proposed in this study includes the spatial layout of samples and the automatic detection of outlier samples. The reasonable spatial distribution of samples is the precondition for improving the accuracy of change detection results, and an automatic detection method for the outlier samples is an important part of automatic change detection technology. In acquiring the samples for change detection, we initially extract a sampling layer from the vector data and use this layer to segment the remote sensing image. In this manner, the image objects can be extracted. Second, samples are extracted from the image objects, and we determine whether the spatial distribution of these samples directly yet reasonably affects the accuracy of the change detection results. On the one hand, the rationality of the sample space layout should reflect the correctness and uniqueness of the samples in typical normal areas. On the other hand, the sample layout must be able to reflect the overall features of the image objects. The sample layout must also be random and uniform, and the samples should be representative. Thus, we extract samples of the image objects in accordance with the sampling region and by considering the distribution and topographic features of these image objects. Then, we detect the outlier image objects in the samples. Local reachability density (LRD) is applied to quantitatively describe the intensity of an image object relative to its neighborhood’s image objects in the feature space. In particular, the LRD algorithm is used to construct an outlier index that can measure the outlier degree of the sample image objects in the feature space. Feature space vectors are also constructed on the basis of the texture feature parameters. Then, the LRDs of the samples are calculated according to reachability distance. The feature space outlier index (FSOI) is also defined and then used to detect the outlier sample objects. Finally, an experimental research on the proposed sampling method was carried out by using a vector topographic map and a high-resolution remote sensing image. The experimental results indicate that the full detection of outlier samples can be achieved when thek value is set from 1/5 to 1/3 of the total number of samples and the FSOI threshold is set to 80%. The proposed sampling method can effectively achieve automatic sampling of change detection samples.

palabra clave

remote sensing image;vector data;sampling;change detection;outlier

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