Edge Detection from High Resolution Remote Sensing Images using Two-Dimensional log Gabor Filter in Frequency Domain

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Wang, K.^1 ; Yu, T.^1 ; Meng, Q.Y.^1 ; Wang, G.K.^1 ; Li, S.P.^1 ; Liu, S.H.^1

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China^1

关键词: Edge detection algorithms;    Frequency domains;    High resolution remote sensing images;    High spatial resolution;    Log-gabor filter;    Remote sensing image processing;    Remote sensing images;    Structural information;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012191/pdf
DOI  :  10.1088/1755-1315/17/1/012191

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Edges are vital features to describe the structural information of images, especially high spatial resolution remote sensing images. Edge features can be used to define the boundaries between different ground objects in high spatial resolution remote sensing images. Thus edge detection is important in the remote sensing image processing. Even though many different edge detection algorithms have been proposed, it is difficult to extract the edge features from high spatial resolution remote sensing image including complex ground objects. This paper introduces a novel method to detect edges from the high spatial resolution remote sensing image based on frequency domain. Firstly, the high spatial resolution remote sensing images are Fourier transformed to obtain the magnitude spectrum image (frequency image) by FFT. Then, the frequency spectrum is analyzed by using the radius and angle sampling. Finally, two-dimensional log Gabor filter with optimal parameters is designed according to the result of spectrum analysis. Finally, dot product between the result of Fourier transform and the log Gabor filter is inverse Fourier transformed to obtain the detections. The experimental result shows that the proposed algorithm can detect edge features from the high resolution remote sensing image commendably.