南水北调与水利科技
南水北調與水利科技
남수북조여수리과기
SOUTH-TO-NORTH WATER
2013年
2期
157-161
,共5页
水系分类%面向对象%多尺度分割%高分辨率遥感影像
水繫分類%麵嚮對象%多呎度分割%高分辨率遙感影像
수계분류%면향대상%다척도분할%고분변솔요감영상
water‐system classification%object‐oriented%multi‐scale segmentation%high‐resolution remote sensing image
高分辨率遥感影像中水系的有效提取,对于实现快速、高效、范围广的整体水系监测是必不可少的.针对山西浑源县的遥感影像,选择最合适的分割尺度和最佳的光谱因子与形状因子(紧致度和光滑性)利用易康软件进行了多尺度分割和面向对象的分类,选取对象的光谱属性信息中的Brightness和对象的几何特征中的length/width两个特征,分别采用最邻近分类法和隶属度函数法进行分类,将遥感影像分为水系和其他地类两种情形.结果表明,面向对象的分类结果要比单纯依靠光谱信息的基于像素的分类方法的结果好,形成的分类结果图更符合人的思维方式.与第二次全国土地调查成果图对比结果和本次分类结果得出的最佳分类结果概率,均表明了最邻近分类法在水系分类中更能准确的进行分类.
高分辨率遙感影像中水繫的有效提取,對于實現快速、高效、範圍廣的整體水繫鑑測是必不可少的.針對山西渾源縣的遙感影像,選擇最閤適的分割呎度和最佳的光譜因子與形狀因子(緊緻度和光滑性)利用易康軟件進行瞭多呎度分割和麵嚮對象的分類,選取對象的光譜屬性信息中的Brightness和對象的幾何特徵中的length/width兩箇特徵,分彆採用最鄰近分類法和隸屬度函數法進行分類,將遙感影像分為水繫和其他地類兩種情形.結果錶明,麵嚮對象的分類結果要比單純依靠光譜信息的基于像素的分類方法的結果好,形成的分類結果圖更符閤人的思維方式.與第二次全國土地調查成果圖對比結果和本次分類結果得齣的最佳分類結果概率,均錶明瞭最鄰近分類法在水繫分類中更能準確的進行分類.
고분변솔요감영상중수계적유효제취,대우실현쾌속、고효、범위엄적정체수계감측시필불가소적.침대산서혼원현적요감영상,선택최합괄적분할척도화최가적광보인자여형상인자(긴치도화광활성)이용역강연건진행료다척도분할화면향대상적분류,선취대상적광보속성신식중적Brightness화대상적궤하특정중적length/width량개특정,분별채용최린근분류법화대속도함수법진행분류,장요감영상분위수계화기타지류량충정형.결과표명,면향대상적분류결과요비단순의고광보신식적기우상소적분류방법적결과호,형성적분류결과도경부합인적사유방식.여제이차전국토지조사성과도대비결과화본차분류결과득출적최가분류결과개솔,균표명료최린근분류법재수계분류중경능준학적진행분류.
@@@@Effective extraction of the information of water system from the high‐resolution remote sensing image is essential for the rapid ,high‐efficiency ,and wide‐range monitoring of the overall water system .In this paper ,the remote sensing images were taken in Hunyuan County of Shanxi Province .The best segmental scale ,spectral factor ,and shape factor (compactness and smoothness) were selected ,and the eCognition software was used to perform multi‐scale segmentation and object‐oriented clas‐sification for the remote sensing images .The Brightness in the spectral properties of the object and length/width in the geome‐try properties of the object were selected for the classification .The remote sensing images were classified into two types ,water system and other type of land ,using the nearest neighbor classification and the membership function classification .The results showed that the object‐oriented classification provides better results than the pixel‐based classification relying solely on the spectral information ,and the classification results were more consistent with the way of human thinking .Through the compari‐son of the land‐use classification maps generated by the second Land Inventory Work and the best classification results obtained in this study ,the nearest neighbor classification offers more accurate classification for the water system .