本文選題：遙感　切入點：高分辨率可見光遙感影像　出處：《光學(xué)學(xué)報》2017年04期 　論文類型：期刊論文

【摘要】：為了提高建筑物提取的自動化程度和精度,提出了一種以分割-分類-優(yōu)化為主線、利用偏移陰影分析的建筑物全自動提取方法。首先,采用面向?qū)ο蟮亩喑叨确指罘椒ㄟM(jìn)行影像初分割;然后,結(jié)合支持向量機(jī)(SVM)分類,將分割結(jié)果分為陰影、植被、建筑物、裸地四大類并提取初始結(jié)果;最后,利用相交邊界陰影比率準(zhǔn)確地驗證了建筑物的存在,剔除了無陰影的非建筑物干擾,獲取了最終結(jié)果。大量的實驗結(jié)果驗證了該方法的有效性,自動化程度得到明顯提高。該方法完整度達(dá)到85%以上,正確率和綜合分?jǐn)?shù)F1均達(dá)到90%以上,且僅需要可見光波段影像數(shù)據(jù),適用范圍廣。
[Abstract]:In order to improve the degree of automation and accuracy of building extraction, presents a segmentation classification optimization as the main line, using the offset shadow analysis of buildings automatic extraction method. Firstly, using object-oriented multi-scale segmentation method for image segmentation; then, combined with support vector machine (SVM) classification, segmentation results as the shadow, vegetation, bare land and buildings, four kinds of initial extraction results; finally, using intersecting boundary and shadowing ratio accurately verify the existence of the building, removing the interference of the buildings without the shadow, to obtain the final results. Experimental results verify the validity of the method, the degree of automation has been improved obviously. The integrity of the above 85%, the correct rate and comprehensive fraction of F1 reached more than 90%, and only need the visible band image data, a wide range of applications.

【作者單位】： 長江大學(xué)地球科學(xué)學(xué)院;長江水利委員會長江科學(xué)院;天津市測繪院;
【基金】：國家自然科學(xué)基金(41671450,41371343) 地理國情監(jiān)測國家測繪地理信息局重點實驗室開放基金(2016NGCM07)
【分類號】：P237;TP751

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本文編號：1645082