  Terahertz imaging shows significant potential across diverse fields, yet the
cost-effectiveness of multi-pixel imaging equipment remains an obstacle for
many researchers. To tackle this issue, the utilization of single-pixel imaging
arises as a lower-cost option, however, the data collection process necessary
for reconstructing images is time-consuming. Compressive Sensing offers a
promising solution by enabling image generation with fewer measurements than
required by Nyquist's theorem, yet long processing times remain an issue,
especially for large-sized images. Our proposed solution to this issue involves
using caustic lens effect induced by perturbations in a ripple tank as a
sampling mask. The dynamic characteristics of the ripple tank introduce
randomness into the sampling process, thereby reducing measurement time through
exploitation of the inherent sparsity of THz band signals. In this study, a
Convolutional Neural Network was used to conduct target classification, based
on the distinctive signal patterns obtained via the caustic lens mask. The
suggested classifier obtained a 95.16 % accuracy rate in differentiating
targets resembling Latin letters.
