计算机应用
計算機應用
계산궤응용
COMPUTER APPLICATION
2009年
11期
3161-3164,3170
,共5页
数字高程模型%填洼%快速排序%时间复杂度%分级
數字高程模型%填窪%快速排序%時間複雜度%分級
수자고정모형%전와%쾌속배서%시간복잡도%분급
Digital Elevation Model ( DEM)%sinks filling%quicksort%time complexity%rank
数字高程模型(DEM)的填洼过程是水系提取中最耗费时间的过程,在Moran和Vezina提出的填洼思想(M&V填洼算法)的基础上,建立了基于快速排序的分级填洼算法,既能有效地减少填洼过程中的搜索路径,提高填洼效率,又能保证依此提取水系的完整与连贯.对于一个给定的DEM地形,传统的填洼算法的执行效率是固定的,而分级填洼的实际执行效率取决于分级数量,对于自然流域,一般200至500的分级量可使计算效率达到最高.通过在6个不同流域上的应用表明,在平均情况下,分级填洼算法的时间复杂度约为O(n~1.29),其执行效率远高于M&V填洼算法及Arcgis 9.2(采用改进Jenson&Domingue算法)的执行效率.
數字高程模型(DEM)的填窪過程是水繫提取中最耗費時間的過程,在Moran和Vezina提齣的填窪思想(M&V填窪算法)的基礎上,建立瞭基于快速排序的分級填窪算法,既能有效地減少填窪過程中的搜索路徑,提高填窪效率,又能保證依此提取水繫的完整與連貫.對于一箇給定的DEM地形,傳統的填窪算法的執行效率是固定的,而分級填窪的實際執行效率取決于分級數量,對于自然流域,一般200至500的分級量可使計算效率達到最高.通過在6箇不同流域上的應用錶明,在平均情況下,分級填窪算法的時間複雜度約為O(n~1.29),其執行效率遠高于M&V填窪算法及Arcgis 9.2(採用改進Jenson&Domingue算法)的執行效率.
수자고정모형(DEM)적전와과정시수계제취중최모비시간적과정,재Moran화Vezina제출적전와사상(M&V전와산법)적기출상,건립료기우쾌속배서적분급전와산법,기능유효지감소전와과정중적수색로경,제고전와효솔,우능보증의차제취수계적완정여련관.대우일개급정적DEM지형,전통적전와산법적집행효솔시고정적,이분급전와적실제집행효솔취결우분급수량,대우자연류역,일반200지500적분급량가사계산효솔체도최고.통과재6개불동류역상적응용표명,재평균정황하,분급전와산법적시간복잡도약위O(n~1.29),기집행효솔원고우M&V전와산법급Arcgis 9.2(채용개진Jenson&Domingue산법)적집행효솔.
A new ranked algorithm to fill sinks in Digital Elevation Models ( DEM) was introduced, including an improved quicksort algorithm to rank DEM data in pieces and a filling algorithm first developed by Olivier Planchon and Frédéric Darboux in 2001 (The idea was first suggested by Moran and Vezina, so it's called the M&V algorithm). Since removing the depressions of a DEM is usually a time-consuming procedure, the new method keeps the integrity and continuity of the drainage networks extracted by the pits-removed DEM and well reduces the depth of seeking path in iteration to accelerate pits-removing process. Different from the constant time complexity of traditional algorithms, the time complexity of the new method is variable, and the best performance for a natural terrain appears to be in 200 to 500 divided-pieces. The application of the new method in 6 study areas shows a time complexity of about O(n~1.29) on average, which is more efficient than the M&V algorithm and Arcgis 9.2 (improved Jenson & Domingue algorithm).