Wavelet-based target detection using multiscale directional analysis

Abstract

Efficient processing of imagery derived from remote sensing systems has become ever more important due to increasing data sizes, rates, and bit depths. This paper proposes a target detection method that uses a special class of wavelets based on highly frequency-selective directional filter banks. The approach helps isolate object features in different directional filter output components. These components lend themselves well to the application of powerful denoising and edge detection procedures in the wavelet domain. Edge information is derived from directional wavelet decompositions to detect targets of known dimension in electro optical imagery. Results of successful detection of objects using the proposed method are presented in the paper. The approach highlights many of the benefits of working with directional wavelet analysis for image denoising and detection.

Multi-Scale Edge Detection of Crack in Extra-High Arch Dam Based on Orthogonal Wavelet Construction

“…Its scale factor and shift factor constitute a time-frequency window that can be expanded and translated. The transformation coefficient in small scale corresponds to the high-frequency component of the signal, while the transformation coefficient in large scale corresponds to the low-frequency component of the signal [19]. Through multiscale decomposition, the signal can be decomposed into components at each frequency, so that the local characteristics of the signal corresponding to different frequencies can be detected.…”