彩色分割的航空影像房屋自动检测

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。

Automatic Houses Detection with Color Aerial Images Based on Image Segmentation

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 automatic 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 segmentation 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 proposed in this paper based on segment growing is in a one-dimensional color space to reduce the computation without human intervention, 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 images, and the experimental results demonstrate that this method has a high precision and rational robustness.