  In point-based sensing systems such as coordinate measuring machines (CMM)
and laser ultrasonics where complete sensing is impractical due to the high
sensing time and cost, adaptive sensing through a systematic exploration is
vital for online inspection and anomaly quantification. Most of the existing
sequential sampling methodologies focus on reducing the overall fitting error
for the entire sampling space. However, in many anomaly quantification
applications, the main goal is to estimate sparse anomalous regions in the
pixel-level accurately. In this paper, we develop a novel framework named
Adaptive Kernelized Maximum-Minimum Distance AKM$^2$D to speed up the
inspection and anomaly detection process through an intelligent sequential
sampling scheme integrated with fast estimation and detection. The proposed
method balances the sampling efforts between the space-filling sampling
(exploration) and focused sampling near the anomalous region (exploitation).
The proposed methodology is validated by conducting simulations and a case
study of anomaly detection in composite sheets using a guided wave test.
