Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms

Liang Huang,Xiaoqing Zuo,Xueqin Yu

doi:10.1155/2017/1793212

Abstract

Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.

Highlights

Edges, an important geometrical feature of remote sensing images, can be used in various processing concerns of remote sensing images, such as registration [1], segmentation [2], classification [3], change detection [4], and fusion [5]
In the experiment 1, Zernike moments and the proposed method are, respectively, applied to carry out edge detection on the two groups of data presented in Figure 3, while Canny operator and the proposed method are adopted to detect the edges of the two groups of data shown in Figure 3, separately
The results obtained using the Zernike moments combined with K-means clustering and Zernike moments combined with fuzzy c-means (FCM) clustering are obviously superior to those obtained merely using Zernike moments

Summary

Introduction

An important geometrical feature of remote sensing images, can be used in various processing concerns of remote sensing images, such as registration [1], segmentation [2], classification [3], change detection [4], and fusion [5]. Traditional edge detection operators include Canny operator, Sobel operator, Susan operator, LOG operator, Laplace operator, and Prewitt operator These are employed to perform edge detection of remote sensing images. (2) Edge detection is based on specific mathematics theories These methods are applied to perform edge detection in remote sensing images using mathematics theories, such as wavelet transform, mathematical morphology, neural network, fuzzy theory, genetic algorithm, and entropy theory. Guan (2001) extracted different kinds of the edge information from remote sensing images through wavelet transform [9]; Xu et al (2008) presented a method for detecting the edges of remote sensing images using a cellular neural network (CNN) [10]; Jubai et al (2006) realized edge detection of remote sensing images by combining fuzzy theory with genetic algorithm [11]; Kiani and Sahebi (2015) performed edge detection of remote sensing images using Shannon entropy [6].

Methods

Results

Conclusion