基于统计学习方法的安徽省安庆市自然资源自动化监测——以山体为例

Automatic monitoring of natural resource in Anqing City of Anhui Province based on statistical learning methods—a case study of mountains

遥感作为一种可以快速、大范围获取地表覆盖信息的技术手段,为复杂的自然资源调查任务提供了可靠的数据来源。针对山体确界问题,以遥感卫星影像为数据支撑,采用非监督的统计学习方法,为山体特征建模。然后,采用DBSCAN算法和边缘检测思想,识别山体区域,并提取山体边界。该方法不依赖于人工标记真值,实现了山体边界的全自动识别。实验采用安庆市Landsat 8遥感卫星影像数据,有效识别了安庆市境内的山体,并提取山体边界。通过定性和定量化分析,验证了方法的可靠性,证明了遥感技术和统计学习理论在自然资源调查领域的应用潜力。该研究方法和结果能够为安庆市明确山体范围,界定山体的完整性与山体保护规划工作提供理论支撑。

Remote sensing,a technology used for quickly and extensively acquisition of land cover information,provides a reliable data source for complex natural resource survey.Aiming at the problem of mountain boundary recognition,an unsupervised statistical learning method was proposed to extract mountain features using remote sensing satellite images for modeling of mountain features.Specifically,DBSCAN algorithm and edge detection ideas were used to identify the mountain area and extract the mountain boundary.This approach recognizes the mountain boundary automatically,which does not rely on marking the ground truth manually.In the experiment,the Landsat 8 remote sensing satellite image data of Anqing City were used to effectively identify the mountainous area and extract the boundaries of the mountains.Through qualitative and quantitative analysis,the reliability of the proposed method was verified.Moreover,it proved the application potential of remote sensing technology and statistical learning theory in the field of natural resource survey.