计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2009年
33期
216-217,226
,共3页
王修信%吴昊%卢小春%吴学军%罗兰娥%朱启疆
王脩信%吳昊%盧小春%吳學軍%囉蘭娥%硃啟疆
왕수신%오호%로소춘%오학군%라란아%주계강
遥感图像%城市绿地提取%混合像元分解%支持向量机(SVM)法%决策树法
遙感圖像%城市綠地提取%混閤像元分解%支持嚮量機(SVM)法%決策樹法
요감도상%성시록지제취%혼합상원분해%지지향량궤(SVM)법%결책수법
remote sensing image%extracting urban green space%mixed pixel decomposing%Support Vector Machine(SVM)%decision tree
从遥感图像提取城市绿地是准确获取城市绿地空间分布的基础.然而由于混合像元的存在,导致城市遥感分类精度不高.因此,利用混合像元分解结合SVM(支持向量机)法提取北京市TM图像城市绿地,并与决策树法比较,研究提高遥感提取城市绿地精度的方法.结果表明,该方法较适合复杂高维空间,对样本选取的准确性没有那么苛刻,可有效地处理城市遥感图像存在的混合像元问题,可较准确地提取城市绿地信息,其精度在92%以上,优于决策树法.
從遙感圖像提取城市綠地是準確穫取城市綠地空間分佈的基礎.然而由于混閤像元的存在,導緻城市遙感分類精度不高.因此,利用混閤像元分解結閤SVM(支持嚮量機)法提取北京市TM圖像城市綠地,併與決策樹法比較,研究提高遙感提取城市綠地精度的方法.結果錶明,該方法較適閤複雜高維空間,對樣本選取的準確性沒有那麽苛刻,可有效地處理城市遙感圖像存在的混閤像元問題,可較準確地提取城市綠地信息,其精度在92%以上,優于決策樹法.
종요감도상제취성시록지시준학획취성시록지공간분포적기출.연이유우혼합상원적존재,도치성시요감분류정도불고.인차,이용혼합상원분해결합SVM(지지향량궤)법제취북경시TM도상성시록지,병여결책수법비교,연구제고요감제취성시록지정도적방법.결과표명,해방법교괄합복잡고유공간,대양본선취적준학성몰유나요가각,가유효지처리성시요감도상존재적혼합상원문제,가교준학지제취성시록지신식,기정도재92%이상,우우결책수법.
Extracting urban green space from remote sensing image is the foundation to get urban vegetation distribution.However, urban classification accuracy is very low because of mixed pixels.Therefore,urban green spaces are extracted from TM image in Beijing based on mixed pixel decomposing and SVM,and compared with those based on decision tree.Great effort is made to en-hance the accuracy.Results from this study show that this method is fit to high dimensional space and sample selection accuracy isn't very strict.h can deal with urban mixed pixels effectively and be used to extract urban green spaces very exactly.The accu-racy of extracting urban green space with this method,which is over 92%,is higher than that with decision tree.
