测绘与空间地理信息
測繪與空間地理信息
측회여공간지리신식
GEOMATICS & SPATIAL INFORMATION TECHNOLOGY
2014年
4期
83-85,93
,共4页
王书玉%张羽威%于振华
王書玉%張羽威%于振華
왕서옥%장우위%우진화
影像分类%随机森林%湿地信息%精度评估
影像分類%隨機森林%濕地信息%精度評估
영상분류%수궤삼림%습지신식%정도평고
image classification%random forests%wetland information%accuracy assessment
随机森林( Random Forests )是一种最有效的分类方法之一。现阶段,它吸引了来自不同领域的研究人员,被广泛应用到不同的学科领域之中。本文采用TM影像,运用随机森林算法,对洪河湿地影像进行分类,并与最大似然监督分类方法( Maximum Likelihood Classification ,MLC)和 CART ( Classification And Regression Tree )算法对比。结果表明,基于RF算法的分类结果的总精度和Kappa系数分别为88.31%和0.82,较MLC和CART分类方法有明显提高。从而证明RF算法可以提高遥感影像的分类精度,并可应用在湿地信息的提取研究中。
隨機森林( Random Forests )是一種最有效的分類方法之一。現階段,它吸引瞭來自不同領域的研究人員,被廣汎應用到不同的學科領域之中。本文採用TM影像,運用隨機森林算法,對洪河濕地影像進行分類,併與最大似然鑑督分類方法( Maximum Likelihood Classification ,MLC)和 CART ( Classification And Regression Tree )算法對比。結果錶明,基于RF算法的分類結果的總精度和Kappa繫數分彆為88.31%和0.82,較MLC和CART分類方法有明顯提高。從而證明RF算法可以提高遙感影像的分類精度,併可應用在濕地信息的提取研究中。
수궤삼림( Random Forests )시일충최유효적분류방법지일。현계단,타흡인료래자불동영역적연구인원,피엄범응용도불동적학과영역지중。본문채용TM영상,운용수궤삼림산법,대홍하습지영상진행분류,병여최대사연감독분류방법( Maximum Likelihood Classification ,MLC)화 CART ( Classification And Regression Tree )산법대비。결과표명,기우RF산법적분류결과적총정도화Kappa계수분별위88.31%화0.82,교MLC화CART분류방법유명현제고。종이증명RF산법가이제고요감영상적분류정도,병가응용재습지신식적제취연구중。
Random Forests is one of the most effective methods of classification .It attracts researchers from different backgrounds and has been widely applied to many disciplines .A Random Forest ( RF) classifier was applied to spectral extracted from Landsat TM im-agery to increase the accuracy of Honghe wetland image classification .The result of RF is compared with the supervised classification techniques including maximum likelihood classification (MLC) and classification and regression tree (CART).This research indi-cates that RF performs relatively better than MLC and CART , providing overall accuracy of 88.31% and kappa values of 0.82.RF can improve the classification accuracy of remote sensing images and can be applied in the study of wetland information extraction .