A Study On Edge Extraction Of Remote Sensing Image Based On Clifford Wavelet

With the development of Remote Sensing technology, massive high-resolution and high-spectral image data have been accumulated. And how to extract important information from these complex image data is the key of its usability. Edge information reflects the main structure and outline of Remote Sensing images by which people can interpret them directly, and it’s also the basis of image segmentation and object recognition. Therefore, edge extraction is one of the important works in interpretation and processing of remote sensing images.A large number of literature and data show that, traditional edge extracting operators have some drawbacks such as translation sensibility, lack of partial direction, shortages in multi-dimensional expression and structure mining and so on. The Clifford Wavelet is based on traditional wavelet, Quaternion and Hilbert transform, which has the advantages of unified multi-dimensional expression and local time-frequency analysis. The paper introduces Clifford Wavelet into edge extraction extending the traditional methods. The remote sensing images are analyzed from scale, phase and direction using Clifford wavelet so as to mine multidimensional character of image information. And therefore, the unified expression and manipulation of three components, extraction of multi-phase information and filtering methods integrating different directions of color images are realized. On basis of these efforts, the paper presents two edge extraction methods—multi-scale, multi-phase integrated and multi-scale, multi-direction integrated methods, which are verified through several cases. The main work includes:(1) The paper compared the principle and feature of Clifford wavelet with that of other transformations including traditional wavelet transform, Wavelet packet transform and multidimensional wavelet transform, and constructed the unified expression of color images based on quaternion. The image was broken down into many components, and every component includes amplitude and three phases. Then the paper extended the computation of phase in traditional wavelet and realized computation of phases of color images on different scales.(2) Traditional edge extraction operators lack detection of part directions and scale analysis. On this basis, this paper researched multi-direction integrated Clifford wavelet filtering method and realized decomposing transformation from eight directions in second level scale. And then, the paper extracted amplitude and phase features of images in different directions and constructed the wavelet decomposing method based on multi-direction structure.(3) The paper proposed two improved algorithms, which are based on above research on scale, phase and direction. The first integrates much phase data in different scales. The second is the integration of multi-scale, multi-directional characteristics after Clifford wavelet. The two algorithms were realized in this paper, and the property and applicability were discussed.(4) Simulation experiments on two groups of remote sensing images were performed, and comparison between new methods and traditional methods was made. The result shows that the first algorithm proposed has great advantages in noise limiting, edge continuity and detail extraction compared with single-phase edge extraction methods. Meanwhile, the second algorithm also has obvious advantages in noise-limiting, edge smoothness, locating accuracy and edge extraction in some directions compared with Sobel operator, Robert operator and traditional wavelet.