基于高光谱高分融合数据在喀斯特地貌环境下的林分分类

喀斯特地区的传统林区保护和统计多靠大量人工测绘及高分影像目视解译勾绘,不仅工作量大耗时耗力,且最终成果精度也不能得到保证.为了尽量弥补这种主观性误差,采用10 m分辨率的高光谱卫星影像与0.8 m分辨率的高分卫星影像多源数据融合的方式,并结合地面目标地物样本光谱采集方法,对贵州六盘水地区乌蒙山地质公园西北段的林分进行分类研究.结果表明,基于高光谱、高分融合影像数据SAM分类方法的分类精度较单一数据源的都高,其中基于融合影像的分类精度为85.9％,基本满足地物分类应用及调绘的精度要求.

Stand Classification Based on Hyperspectral High Resolution Fusion Data in Karst Environment

The traditional forest protection and statistics in karst area mostly rely on a large number of manual mapping and high resolution image visual interpretation, which not only consumes a lot of time and effort, but also can not guarantee the accuracy of the final results. In order to make up for this subjective error, introduces multi-source data fusion of 10 m resolution hyperspectral satellite image and 0.8 m resolution high resolution satellite image,and combines with the spectral acquisition method of ground object samples, to study the forest classification of Wumengshan Geopark in Liupanshui area of Guizhou Province,The results show that the classification accuracy of SAM based on hyperspectral and high-resolution fusion image data is higher than that of single data source, and the classification accuracy based on fusion image is 85.9%, which basically meets the requirements of surface feature classification and application The precision requirement of adjustment and drawing.