CAJ | 학술논문

航空影像房屋提取方法的研究中大多基于灰度影像的区域生长算法，此类算法不仅忽略了不同材质的房屋所呈现的光谱特征对提取结果的影响，而且过于依赖种子像素的选取，处理效率不高。为了从高分辨率航空影像中实现房屋的自动检测，综合利用彩色信息与屋顶材料的光谱特征，采用影像分割原理，研究了房屋自动检测的方法。首先对RGB与HIS彩色空间进行转换，利用HIS空间各分量间不相关的特点和屋顶材料光谱特征进行影像分割，分离出红色瓦片屋顶与灰色水泥屋顶区域，并利用标记分水岭算法实现房屋区域的初始分割；然后计算各标记区域内的色调均值选取种子像斑样本，进而以像斑为单元在色调分量中进行区域生长，最后经过消除小斑和矩形拟合优化处理，得到轮廓清晰的房屋区域。与传统的基于像素区域分割算法相比，该方法整个过程无需人工干预且均在一维彩色空间进行处理，计算量明显降低，同时采用改进的基于像斑区域生长算法能够兼顾邻近区域内像素的几何结构信息，使算法精度得到显著提高，采用上述方法对高分辨率航空影像进行了实验，结果证明该方法有着较高的处理效率和准确性，具有实用价值。
항공영상방옥제취방법적연구중대다기우회도영상적구역생장산법，차류산법불부홀략료불동재질적방옥소정현적광보특정대제취결과적영향，이차과우의뢰충자상소적선취，처리효솔불고。위료종고분변솔항공영상중실현방옥적자동검측，종합이용채색신식여옥정재료적광보특정，채용영상분할원리，연구료방옥자동검측적방법。수선대RGB여HIS채색공간진행전환，이용HIS공간각분량간불상관적특점화옥정재료광보특정진행영상분할，분리출홍색와편옥정여회색수니옥정구역，병이용표기분수령산법실현방옥구역적초시분할；연후계산각표기구역내적색조균치선취충자상반양본，진이이상반위단원재색조분량중진행구역생장，최후경과소제소반화구형의합우화처리，득도륜곽청석적방옥구역。여전통적기우상소구역분할산법상비，해방법정개과정무수인공간예차균재일유채색공간진행처리，계산량명현강저，동시채용개진적기우상반구역생장산법능구겸고린근구역내상소적궤하결구신식，사산법정도득도현저제고，채용상술방법대고분변솔항공영상진행료실험，결과증명해방법유착교고적처리효솔화준학성，구유실용개치。
In order to achieve housing automatic detection from high-resolution aerial imagery ,the present paper utilized the color information and spectral characteristics of the roofing material ,with the image segmentation theory ,to study the housing auto-matic detection method .Firstly ,This method proposed in this paper converts the RGB color space to HIS color space ,uses the characteristics of each component of the HIS color space and the spectral characteristics of the roofing material for image segmen-tation to isolate red tiled roofs and gray cement roof areas ,and gets the initial segmentation housing areas by using the marked watershed algorithm .Then ,region growing is conducted in the hue component with the seed segment sample by calculating the average hue in the marked region .Finally through the elimination of small spots and rectangular fitting process to obtain a clear outline of the housing area .Compared with the traditional pixel-based region segmentation algorithm ,the improved method pro-posed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human inter-vention ,and can cater to the geometry information of the neighborhood pixels so that the speed and accuracy of the algorithm has been significantly improved .A case study was conducted to apply the method proposed in this paper to high resolution aerial ima-ges ,and the experimental results demonstrate that this method has a high precision and rational robustness .