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Diversity features collaboration technology for monitoring forests before and after hurricanes by remote sensing

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

  • Email：xzhong7@stu.xidian.edu.cn
  • Introduction：钟娴，研究方向为遥感图像处理。E-mail： xzhong7@stu.xidian.edu.cn
ZHONG Xian1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

  • Email：wfeng@xidian.edu.cn
  • Introduction：冯伟，研究方向为遥感图像处理、机器学习。E-mail： wfeng@xidian.edu.cn
FENG Wei1*,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

ZHANG Yali1,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

QUAN Yinghui1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

HUANG Wenjiang2,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China

XING Mengdao13

Resümee

Change monitoring and disaster assessment of hurricane-damaged forests are important applications of remote sensing technology. The extraction of feature information from remote sensing images is very important to forest remote sensing monitoring. The combination of diversity features can effectively improve the accuracy of forest change monitoring. However, the current spatial information acquisition algorithms, such as texture features, still retain the traditional fixed computing model and do not fully consider the diversity of the spatial distribution of ground objects. When extracting texture features, the accuracy of texture features is affected when the sliding window is extremely large or small. Therefore, the calculation method of this kind of texture feature is always faced with the problem of hard balance between the number of features and the neighborhood reference range. Therefore, this paper focuses on forest change monitoring technology. To solve the above problems, a remote sensing monitoring method of forest destruction before and after hurricanes are proposed based on diversity features collaborative technology. First, the difference between the normalized vegetation index (NDVI) and the Enhanced Vegetation Index (EVI) before and after forest remote sensing image change is calculated. Second, the compound window technology is proposed to extract the texture features. Then, the texture features extracted from the remote sensing image and the spectrum of the remote sensing image are used to build a diverse characteristic combination model. This model can enhance the diversity of features. Finally, an improved rotating forest algorithm based on feature separation is proposed to reduce the direct feature correlation and improve the accuracy of the classifier. The study area is the remote sensing images of the Nezer forest in France before and after the hurricane, which were obtained from the Formosat-2 satellite. In the experimental part, the classification performance of the proposed algorithm was compared with those of six other methods. Experimental results show that compared with the traditional change detection methods based on spectral and texture features, the overall accuracy of the proposed method, the detection accuracy of the changed area, and the unchanged area improved by 3.68%, 6.53% and 3.46%, respectively. In addition, the sensitivity of the features extracted by different methods to the number of training samples was tested. The results show that the proposed method maintains high classification accuracy on different training sample numbers, and its overall accuracy and Kappa coefficient are better than those of the comparison methods. After the number of training samples reaches 50, the accuracy of the proposed method tends to flatten. The proposed method can effectively improve the performance of forest change monitoring. This method can be used to monitor the change of and damage to forests in real time and efficiently obtain information on forest disaster areas, providing important reference data for the emergency decision-making of forest resource management departments. Therefore, this method has a high practical value.

Schlüsselwort

multi features;classification;rotation forest;forest monitoring;vegetation index;Formosat-2 satellite

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