【摘要】：針對(duì)高分影像陰影檢測精度易受水體、深色地物和暗色植被影響等問題,結(jié)合GF-1影像自身特點(diǎn),提出一種結(jié)合特征分量構(gòu)建和多尺度分割面向?qū)ο蟮年幱皺z測方法。首先,對(duì)GF-1影像多光譜數(shù)據(jù)、全色數(shù)據(jù)進(jìn)行正射校正和信息融合,以達(dá)到光譜與空間分辨率信息最大化利用。其次,集成特征分量(主成分第一分量PC1、亮度分量V、綠光波段G、歸一化植被指數(shù)NDVI)以增強(qiáng)陰影信息。最后,對(duì)集成后的影像進(jìn)行多尺度分割,并利用特征分量構(gòu)建規(guī)則集,最終實(shí)現(xiàn)陰影信息提取。實(shí)驗(yàn)表明,該方法既能準(zhǔn)確地檢測出GF-1影像中的陰影信息,又能有效削弱水體、深色地物和暗色植被的影響。
[Abstract]:In order to solve the problems that the accuracy of shadow detection in high score images is easily affected by water body, dark ground objects and dark vegetation, combined with the characteristics of GF-1 images, an object-oriented shadow detection method combining feature component construction and multi-scale segmentation is proposed. Firstly, the multispectral data and panchromatic data of GF-1 images are corrected and information fusion is carried out in order to maximize the utilization of spectral and spatial resolution information. Secondly, the feature components (principal component PC1, luminance component V, green light band G, normalized vegetation index NDVI) are integrated to enhance shadow information. Finally, the integrated image is segmented by multi-scale, and the rule set is constructed by using feature components, and finally shadow information extraction is realized. The experimental results show that this method can not only accurately detect the shadow information in GF-1 images, but also effectively weaken the influence of water body, dark ground objects and dark vegetation.
【作者單位】： 蘭州交通大學(xué)測繪與地理信息學(xué)院;甘肅省地理國情監(jiān)測工程實(shí)驗(yàn)室;甘肅省遙感重點(diǎn)實(shí)驗(yàn)室;
【基金】：甘肅省科技計(jì)劃資助(1506RJZA070,148RJZA028) 甘肅省高等學(xué)�？蒲许�(xiàng)目(2015A-049) 甘肅省遙感重點(diǎn)實(shí)驗(yàn)室(寒旱所)開放基金(2015-RC-28)
【分類號(hào)】：TP751

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