CAJ | 학술논문

高分辨率遥感影像的应用越来越多，但其高昂的成本让一般项目望而却步．应用软件从Google Earth上下栽已成为获取高空间分辨率影像的有效途径，但因无光谱信息，解译局限较大．选用C4．5算法的决策树方法，对目标为水塘研究的广州市天河区的下载的快鸟数据进行解译，与最大似然分类法和面向对象分类法相比较．结果表明：决策树分类法的分类精度和kappa系数均较高，能利用多源数据，结构简单直观，易于表达和应用；提取小目标地物更有效，数据量相对小．速度较快．
고분변솔요감영상적응용월래월다，단기고앙적성본양일반항목망이각보．응용연건종Google Earth상하재이성위획취고공간분변솔영상적유효도경，단인무광보신식，해역국한교대．선용C4．5산법적결책수방법，대목표위수당연구적엄주시천하구적하재적쾌조수거진행해역，여최대사연분류법화면향대상분류법상비교．결과표명：결책수분류법적분류정도화kappa계수균교고，능이용다원수거，결구간단직관，역우표체화응용；제취소목표지물경유효，수거량상대소．속도교쾌．
The applications of high resolution remote sensing image in many fields become increasingly helptul, but the cost is formidable. Downloading high spatial resolution image from the Google Earth by applications of software has become an effective way. However, because there is no spectral information in the downloaded data, so the interpretation is limited. On the basis of mainstream remote sensing method, the paper selected the decision tree method by C4.5 algorithm, to interpret quick bird image of Tianhe district of Guangzhou city, which projected target to the pond, and then compared results of the maximum likelihood classification method and object-oriented classification. Analysis showed that the decision tree classification method had the following results： both the overall accuracy and Kappa coefficients were higher; it could reduce consumption of knowledge establishment and practice; it could make use of multi-source data; it was simple and intuitive structure, easy to express and application; the extracted small target features was more effectively, data volume was small relatively; it run fast.