CAJ | 학술논문

森林训练样本自动提取算法（ TDA）已在Landsat图像分析中得到了成功应用，笔者以广西苍梧县广平镇为研究区，采用2007年ALOS、2011年RapidEye遥感图像，试验该算法在高分辨率图像中的应用。研究首先根据图像光谱特性自动识别出纯净森林训练样本，然后依据归一化的整合森林指数图像提取两期森林／非森林分类结果并以此进行林地变化检测，经过精度分析结果表明，面积总误差为－2．6％，空间位置精度为87．7％，说明该算法可有效地从高分辨率遥感图像提取出纯净的森林训练样本，为森林／非森林分类以及变化检测提供基础数据。
삼림훈련양본자동제취산법（ TDA）이재Landsat도상분석중득도료성공응용，필자이엄서창오현엄평진위연구구，채용2007년ALOS、2011년RapidEye요감도상，시험해산법재고분변솔도상중적응용。연구수선근거도상광보특성자동식별출순정삼림훈련양본，연후의거귀일화적정합삼림지수도상제취량기삼림／비삼림분류결과병이차진행임지변화검측，경과정도분석결과표명，면적총오차위－2．6％，공간위치정도위87．7％，설명해산법가유효지종고분변솔요감도상제취출순정적삼림훈련양본，위삼림／비삼림분류이급변화검측제공기출수거。
The algorithm of forest training data automation( TDA) has been successfully applied to Landsat images. Tak?ing Guangping Town, Cangwu County, Guangxi Province as the study area, we selected the ALOS image of 2007 and the RapidEye image of 2011 to explore the algorithm?s application in high resolution remote sensing images. The pure forest training samples were automatically identifed at first, and the change detection result was then obtained by the forest/non?forest classification which extracted by the normalized integrated forest index image involved in the anlaysis.The ac?curate evaluation results showed that the total area error was-2.6% and the spatial location accuracy was 87.7%. It was shown that this algorithm could be effectively applied to high resolution remote sensing images to extract pure forest train?ing samples for the forest/non?forest classification and change detection as the original data.