【摘要】：基于模板匹配的道路跟蹤是半自動提取道路的主要方法。然而場景中地物干擾和道路寬度的變化降低了模板匹配的穩(wěn)定性;另外,道路跟蹤失敗后缺乏重檢測機(jī)制,使得道路提取過程中人機(jī)交互頻繁。針對以上問題,提出了一種基于P-N(positive-negative)學(xué)習(xí)的高分遙感影像道路半自動提取方法。該方法由道路跟蹤、檢測和學(xué)習(xí)構(gòu)成,關(guān)鍵是采用了P-N學(xué)習(xí)的策略迭代的訓(xùn)練分類器,通過糾正違反結(jié)構(gòu)約束的樣本分類結(jié)果來提高分類器性能。實(shí)驗(yàn)使用了不同場景下的城區(qū)高分遙感影像,與經(jīng)典的模板匹配和在線學(xué)習(xí)的道路跟蹤方法進(jìn)行了比較。實(shí)驗(yàn)結(jié)果表明該方法在道路提取的精度和穩(wěn)定性方面均有提升。
[Abstract]:Road tracking based on template matching is the main method of semi-automatic road extraction. However, the variation of ground objects and road width in the scene reduces the stability of template matching. In addition, the lack of re-detection mechanism after road tracking failure makes the human-computer interaction frequent in the road extraction process. Aiming at the above problems, a semi-automatic road extraction method for high score remote sensing images based on P-N (positive-negative) learning is proposed. The method is composed of road tracking, detection and learning. The key is to use the P-N learning strategy iterative training classifier to improve the performance of the classifier by correcting the classification results of samples that violate the structure constraints. The high score remote sensing images of different scenes are used in the experiment, and compared with the classic template matching and online learning road tracking methods. The experimental results show that this method can improve the accuracy and stability of road extraction.
【作者單位】： 武漢大學(xué)測繪遙感信息工程國家重點(diǎn)實(shí)驗(yàn)室;重慶市勘測院;武漢大學(xué)遙感信息工程學(xué)院;
【基金】：國家973計(jì)劃(2012CB719906) 高分辨率對地觀測系統(tǒng)重大專項(xiàng)~~
【分類號】：P237
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本文編號：2141694