| Wavelet analysis complements Fourier analysis where time information is needed. Fourier Transform gives spectral components but no information regarding where in time those spectral components appear. Wavelet analysis can present a time-scale view of the signal instead.; Both CWT---Continuous Wavelet Transform and DWT---Discrete Wavelet Transform are used in spatial data analysis. CWT is computed by changing the scale of wavelet, shifting the window in time, multiplying by the signal, and integrating over all times. The CWT tool in MATLAB provides a wavelet coefficient scalogram which helps in finding particular patterns. Filters of different cutoff frequencies are used to analyze the signal at different scales in DWT.; Before implementing CWT and DWT on the spatial data, interpolation is applied to the datasets to get equally spaced experiment data. We identify the features of the spatial data by comparing the analysis results of the adjacent Easting value data vector. |
