河床質調查最重要之目的在於暸解河床粒徑分佈資訊,ㄧ般傳統 河床質調查有體積、網格與面積法等,可依據調查目的不同,選用不同的調查方法,如表層分佈與底層分佈;目前水利界常用的為體積法,然而此法工作量龐大,費時費力,往往需要許多的人力、物力投入其中,造成許多資源的浪費;近年來照相與影像處理技術進步迅速,在辨別與量測方面皆有良好的成果,可作為量測河床粒徑分佈的有利工具;本研究以固定面積拍攝所得影像,透過製作標誌(marker)為基礎,提出四個主要步驟:(1)影像預處理,(2)標誌製作,(3)影像切割,(4)測量最大短軸;上述方式可抑制分水嶺演算法過度切割的缺點並獲得良好的分割結果;我們以台灣北部景美溪所採樣的石頭帶回實驗室隨機排列,並拍攝影像進行分析,藉由上述方法,推算其所得個數及粒徑百分比累積曲線,與實驗室篩分析所得個數及粒徑百分比累積曲線相比有良好的結果,可作為後續研究之參考及快速研判河床粒徑分佈之用。
The measurement techniques of river materials are mainly to get surface grain-size distribution information. There are several traditional measurement techniques, such as volume, grid, and area measurement methods. Among them, the volume measurement method is the most common method used by the Hydraulics, but this method needs a huge workload, time and energy. Image analysis techniques have been shown to work well in identifying and measuring particles, consequently they can be powerful tools for measuring the grain size distributions. In this paper we present a rapid image-processing-based procedure for the measurement of exposed fluvial gravels, defining the steps required to minimize the errors in the derived grain size distribution. The main procedure is divided into four steps: (1)image pre-processing, (2)marker making, (3)image segmenting, and (4)maximum b-axis measuring. The analyzed stones were obtained from Jingmei River and randomly disposed within a square meter grid in the laboratory and taken picture for the analysis. The measurement errors compared with sieve analyses are quite small in all the cases, consequently we can conclude that the image processing method proposed in this study can efficiently and precisely identify the grain-size distribution and can be used in the follow-up research.