摘要
目的针对高分辨率遥感影像普遍存在的同谱异物和同物异谱问题,提出一种综合利用光谱、形状、空间上下文和纹理特征的建筑物分级提取方法。方法该方法基于单幅高分辨率遥感影像,首先利用多尺度多方向梯度算子构造的建筑物指数和形状特征提取部分分割完整的矩形建筑物目标;然后由多方向线性结构元素和形态学膨胀运算确定投票矩阵,从而获取光照方向,并利用光照方向和阴影特征对已提取建筑物进行筛选,剔除非建筑物对象,完成建筑物初提取;最后借助初提取建筑物对象的纹理特征向量建立概率模型,取得像素级建筑物提取结果,将该结果与影像分割相结合实现建筑物提取。结果选取两幅高分辨率遥感影像进行实验,在建筑物初提取实验中,将本文方法与邻域总变分法和Sobel算子进行对比,实验结果表明,本文方法适用性强,为后提取提供的建筑物样本可靠性更高。在建筑物提取实验中,采用查准率、查全率和F_1分数3个指标进行定量分析,与形态学建筑物指数结合形态学阴影指数算法、邻域总变分结合混合高斯模型和贝叶斯判决算法相比,各项精度指标均得到显著提升,其中查准率提高了2.90个百分点,查全率提高了12.49个百分点,F_1分数则提升了8.84。结论本文提出的建筑物分级提取方法具备一定抗干扰能力,且提取准确性高,适用性强。
Objective Buildings are important infrastructures in geodatabase. Extracting buildings effectively and accurately is significant in urban planning, population distribution, land analysis, and environmental surveys. However, buildings cannot be extracted using a unified model because of the common phenomenon of ＂same object with different spectra and same spectrum with different objects＂ and given that the spectrum, shape, and texture of a building are not exactly the same. Considering that most buildings are in rectangular shapes and that the spectral information of a building is generally more uniform than that of other objects, some buildings can be extracted with shape and gradient information. On this ba- sis, the open space and waters are removed using the shadow as a context feature. The texture of the imagery within build-ings usually presents a good consistency within a certain area. Thus, other buildings can be extracted by the probability model, which is established by textural features from the initial extraction of buildings as the training samples. On the basis of the analysis above, a hierarchical building extraction method based on spectral, spatial, contextual, and textural features is proposed. Method The proposed method includes three main parts, namely, image preprocessing, initial extraction of buildings, and final extraction of buildings, and is applied to single high-resolution remote sensing imagery. High-frequency noise is removed and edge information is effectively retained by imagery preprocessing based on bilateral filter. Vegetation and shadow are extracted using super green and morphological shadow indexes. The building index is constructed using multi-scale and multi-orientation gradient operators, and some rectangle buildings are extracted using the building index and shape features. The extracted buildings are expanded with the multi-direction linear structural elements. The voting matrix is calculated by the number of pixels included in the intersection of the expansion result
出处
《中国图象图形学报》
CSCD
北大核心
2017年第12期1798-1808,共11页
Journal of Image and Graphics
基金
国家自然科学基金项目(41601507)
地理信息工程国家重点实验室开放基金项目(SKLGIE2015-M-3-3)~~
关键词
高分辨率遥感影像
建筑物提取
多特征融合
建筑物指数
高斯模型
high resolution remote sensing imagery
building extraction
malti-feature fusion
building index
Gaussian model
