Remote Sensing Image Change Detection Technology Based on GDAL

doi:10.3778/j.issn.1002-8331.1907-0228

Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (16): 169-175.DOI: 10.3778/j.issn.1002-8331.1907-0228

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Remote Sensing Image Change Detection Technology Based on GDAL

JIANG Shihao, JIANG Hong

1.The Academy of Digital China（Fujian）, Fuzhou University, Fuzhou 350108, China
2.Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

Online:2020-08-15 Published:2020-08-11

基于GDAL的遥感图像变化检测技术

蒋世豪，江洪

1.福州大学数字中国研究院（福建），福州 350108
2.福州大学空间数据挖掘与信息共享教育部重点实验室，卫星空间信息技术综合应用国家地方联合工程研究中心，福州 350108

Abstract

Abstract:

As GDAL（Geospatial Data Abstraction Library） can quickly read remote sensing images in various formats and parse spatial metadata effectively, it has obvious advantages in developing remote sensing image processing algorithm. In this paper, a set of remote sensing technology for vegetation change detection in mountain areas with complex terrain is developed, which combined with GDAL and corresponding algorithms. Among them, vegetation growth is inverted by Shadow Elimination Vegetation Index（SEVI）; image difference method and OTSU are used to extract the points of significant change in vegetation growth; [K]-means clustering is used to automatically segment and identify the changing regions. The method is applied to the detection and testing of Landsat8 OLI remote sensing images in Wuyi Mountain Nature Reserve and Minjiangyuan Nature Reserve among 2016 to 2017. The results show that the technology of remote sensing image change detection is feasible, can effectively identify the remote sensing image change area, and has good application value in the monitoring of vegetation growth in mountainous areas with complex terrain.

Key words: remote sensing images, change detection, cluster analysis, vegetation growth, GDAL, Shadow Elimination Vegetation Index（SEVI）

摘要：

由于GDAL（Geospatial Data Abstraction Library）具有快速读取多种格式的遥感图像且能有效解析空间元数据等特点，利用它开发遥感图像处理算法具有明显的优势。结合GDAL及相应算法，开发了一套复杂地形山区植被遥感变化检测的技术，其中包括利用阴影消除植被指数（Shadow Elimination Vegetation Index，SEVI）反演植被长势；利用图像差值法及最大类间方差法（OTSU）来提取植被长势明显变化点位；利用[K]均值聚类自动分割识别变化区域。将该方法用于武夷山自然保护区和闽江源自然保护区2016—2017年Landsat8 OLI遥感图像的植被长势变化检测，结果表明，这套遥感图像变化检测技术切实可行，能够有效识别遥感图像变化区域，并在复杂地形山区的植被长势监测中具有良好的应用价值。

关键词: 遥感图像, 变化检测, 聚类分析, 植被长势, GDAL, 阴影消除植被指数（SEVI）

JIANG Shihao, JIANG Hong. Remote Sensing Image Change Detection Technology Based on GDAL[J]. Computer Engineering and Applications, 2020, 56(16): 169-175.

蒋世豪，江洪. 基于GDAL的遥感图像变化检测技术[J]. 计算机工程与应用, 2020, 56(16): 169-175.

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Remote Sensing Image Change Detection Technology Based on GDAL

基于GDAL的遥感图像变化检测技术

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