本文選題：高分辨率　切入點：遙感影像　出處：《儀器儀表學(xué)報》2017年09期 　論文類型：期刊論文

【摘要】：如何減小分割結(jié)果與實際地理對象間的差異,是目前高分辨遙感影像分割中面臨的一個難點問題。為此,構(gòu)建了一種新的對象置信度(OC)指標來衡量任意區(qū)域與地理對象間的匹配程度,進而提出了一種面向地理對象的多尺度分割算法。該算法主要包括兩個步驟:首先,通過對影像進行過分割來構(gòu)建初始種子區(qū)域集合,并確定尺度參數(shù)集合;而后,通過跟蹤對象置信度指標OC的尺度間變化來指引多尺度區(qū)域合并過程,使區(qū)域合并結(jié)果逐步逼近實際的地理對象。多組實驗結(jié)果表明,所提出的算法能夠顯著改善過分割及欠分割問題,準確識別建筑物、道路等地理對象的完整輪廓,在定性分析及定量精度評價中均顯著優(yōu)于商業(yè)軟件e Congnition及傳統(tǒng)多尺度分割算法。
[Abstract]:How to reduce the difference between segmentation results and actual geographic objects is a difficult problem in high-resolution remote sensing image segmentation. In this paper, a new object confidence index is constructed to measure the matching degree between any region and geographical object, and then a multi-scale segmentation algorithm for geographic object is proposed. The initial seed region set is constructed by over-segmentation of the image, and the set of scale parameters is determined. Then, the process of multi-scale region merging is guided by tracking the inter-scale variation of the confidence index OC of the object. The results of multiple experiments show that the proposed algorithm can significantly improve the over-segmentation and under-segmentation problems and accurately identify the complete contour of geographic objects such as buildings and roads. In qualitative analysis and quantitative accuracy evaluation, it is superior to commercial software e Congnition and traditional multi-scale segmentation algorithm.
【作者單位】： 南京信息工程大學(xué)電子與信息工程學(xué)院;河海大學(xué)計算機與信息學(xué)院;
【基金】：國家自然科學(xué)基金(61601229) 江蘇省自然科學(xué)基金(BK20160966) 江蘇省高校自然科學(xué)基金(16KJB510022) 東南大學(xué)移動通信國家重點實驗室開放研究基金(2012D20)項目資助
【分類號】：TP751
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本文編號：1583843