查詢結果分析
相關文獻
- 以區塊化物件分類法萃取ADS-40影像中水稻田坵塊資訊之研究
- 利用物件式導向進行崩塌地種類判釋、復育追蹤--以高雄市寶來地區為例
- 口袋裏的小精靈--Java Card漫談
- 電信網路管理之SDH Q3 Agent探討
- Fortran 90實現物件導向繼承功能之探討
- Object-Oriented Design Methodology and Implementation of an Optimizing Adaptive Quality Controller for Semiconductor Manufacturing Processes
- C++Fuzzy類神經網路物件導向發展系統之建立
- Providing an Object-Relational Interface for a Relatioanl Database
- 建置多媒體資料庫管理系統之需求探討
- Internet計算環境系列(2)--分散式物件標準初探
頁籤選單縮合
題 名 | 以區塊化物件分類法萃取ADS-40影像中水稻田坵塊資訊之研究=The Study on Regional Object Classification for Paddy Rice Patch Extraction from ADS-40 Image |
---|---|
作 者 | 雷祖強; 李哲源; 葉惠中; 萬絢; | 書刊名 | 航測及遙測學刊 |
卷 期 | 14:2 2009.06[民98.06] |
頁 次 | 頁127-140 |
分類號 | 440.98 |
關鍵詞 | 物件; 區域成長法; 影像判釋; Object; Region growing method; Image recognition; |
語 文 | 中文(Chinese) |
中文摘要 | 影像應用時最為重要的研究議題就是影像分類。基本上人類分類影像程序是將影像中區塊特徵(Region Feature)萃取出來,合併其特徵後進而形成有效的識別物件。然而,目前機器分類影像時是以逐像元概念(Pixel-based)進行特徵萃取之工作,兩者程序並不相同。為解決上述問題本研究提出以區塊為主(Region-based)的區塊化物件分類 (Regional Object Classification, ROC)法,這是一個混合非監督程序(區域成長法)與監督式程序(最大概似分類法)的新概念,這個概念可解決逐像元分類法應用於高解析度影像時所造成之嚴重的椒鹽效應(Salt and Pepper Effect)。本研究在雲林縣農業區內選擇高解析度數值航照影像(ADS-40),針對水稻這個類別作為實證項目,以逐像元與區域化分類概念進行問題比較。研究結果顯示,透過區塊化物件分類模式可萃取出完整度極高的水稻坵塊田,分類精度可從92.6% (Pixel-based)提高到95.3% (Region-based)。 |
英文摘要 | The image classification is the most important issue in Remote Sensing. In essence, extraction of regional feature is an important process on human’s image classification process to form their effective identification of the target objects. However, vision cognition of machine learning follows Pixel-based which disobeys the rule of human’s image classification process. To resolve the forgoing problems, this study proposes a Region-based approach for Regional Object Classification (ROC). This method is a hyper module which applies an unsupervised method (Region-Growing, RG) to a supervised approach (Maximum Likelihood Classifier, MLC). New concept can effectively reduces a Salt and Pepper effect of classification result from very high resolution image by conventional pixel base classifier. Accordingly, a module of Regional concept is applied to improve its classification accuracy. Our outcomes are used by mean of the ADS-40 data to present the classification performance. Finally, this study utilized the ROC classifier and the overall accuracy of region based concepts (95.3%) is better than those of the pixel-based concepts (92.6%) on the evaluation of paddy rice from ADS-40 image. This result shows that an appropriate progress through regional module can effectively improve the accuracy of image classification. |
本系統中英文摘要資訊取自各篇刊載內容。