Detection of High Resolution Remote Sensing Imagery Using Wavelet

摘要

In this paper, The 3-.level B-Spline wavelet transform is applied to extract different types of edges, according to the singularity exponent of edges from high resolution remote sensing imagery. Gradient algorithm, Laplacian algorithm, Robert algorithm are classic linear algorithms. However these algorithms have an acute function on edges and are vulnerable to noise. In the process of edge detection using wavelet, edge information at different scales is obtained according to wavelet multi-scale's character. We may integrate multi-scale edge information to create high accuracy and different types of edges with a pixel width. In this paper, I take over 3-level B-Spline wavelet function. We also verify the 3-level B-Spline wavelet transform being asymptotically optimum in the practical application such as feature extraction. This paper gives out its fast algorithm in decomposition, response in time and frequency analyze. More importantly, in this paper, the algorithm can also be used to determinate the singularity exponent of edges so as to identify different types of edges. According to the different needs, we can output the different types of edges that serve as a more effective representation on which subsequent localization and recognition tasks are based.