摘要
现有的居民地检测方法大多是基于影像的纹理、光谱、形状等特征。当影像出现尺度、光照等条件变化时,将导致这些特征出现变化,造成算法的稳健性下降,而局部不变特征(如角点)却不易受到这些因素的影响。为此,提出一种基于角点特征的高分辨率遥感影像居民地检测方法。该方法首先在Harris算子提出角点的基础上,加入两个约束准则检测居民地的角点,然后根据影像中角点的分布情况,自适应地构建似然函数来度量影像中每一个像素属于居民地的概率,最后采用二值分割方法提取影像中的居民地。试验结果表明。本文方法可以快速、可靠地检测到影像中的居民地区域,具有较高的实际应用价值。
Traditional residential area detection methods are mainly based on image features, such as texture, spectrum, shape and etc. However, these features are not invariant to scale and illumination changes, which consequently reduce the robust of the existing algorithms. To solve this problem, the proposed method uses local feature for residential area detection from high-resolution remote-sensing imagery, which consists of three steps. Firstly, a large set of local feature points are extracted by Harris corner detector. In order to achieve a reliable extrac- tion of corners from residential areas, two criterions are further proposed to validate and filter them. Afterwards, the extracted corners are incorporated into a likelihood function, and are used to measure the possibility of each pixel belonging to the residential area. Finally, residential areas are extracted by an adaptive binary segmentation method. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy.
出处
《测绘学报》
EI
CSCD
北大核心
2014年第2期164-169,192,共7页
Acta Geodaetica et Cartographica Sinica
基金
国家973计划(2012CB719903)
国家自然科学基金(41301453)
关键词
高分辨率遥感影像
居民地检测
角点提取
概率似然函数
high-resolution remote sensing imagery
residential area extraction
corner detec-tion
probability likelihood function
