提出一种基于频域滤波的城市河道信息提取方法。首先对高分辨率遥感图像进行傅里叶变换得到频谱图, 并利用径向和角向分布图分析城市河道的频谱特征。其次, 基于城市河道的双线型特点, 将其分为边缘特征和低频信息两个部分, 并根据周期性纹理的频谱模型和地物频谱能量分布规律确定两个部分的频域识别标志。然后设计相应的扇环形带通log Butterworth滤波器和低通Butterworth滤波器分别对城市河道的边缘特征和低频信息进行提取, 并根据该两部分信息实现城市河道信息提取。最后对城市河道信息提取结果进行定量评价, 结果表明, 本文方法可以有效地实现城市河道的信息提取。

We propose a new method for extracting urban rivers from high resolution remotely sensed imagery in the frequency domain. Radial and angular samplings are used to analyze the magnitude spectrum (frequency spectrum) signature of the urban river after discrete Fourier transformation of the remotely sensed imagery into the frequency spectrum. The bilinear features of the urban river in high resolution remotely sensed imagery are divided into two parts: edge features and low frequency information. The frequency recognition marks of these two parts are retrieved based on the periodic texture frequency spectrum model and the spectrum energy discipline of ground objects. Meanwhile, arc ring two dimensional log Butterworth band-pass filter and two dimensional Butterworth low-pass filter are designed to extract edge features and low frequency information, respectively. The two extraction results can be used in extracting urban rivers effectively from highresolution remotely sensed imagery. Evaluation of the extraction results shows that the proposed algorithm is effective in extracting urban rivers from high spatial resolution remotely sensed imagery.