Open AccessLearned linear models for detecting watercraft in a maritime environment
Author(s) -
Colin C. Olson,
Jonathan M. Nichols
Publication year - 2020
Publication title -
applied optics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.668
H-Index - 197
eISSN - 2155-3165
pISSN - 1559-128X
DOI - 10.1364/ao.396496
Subject(s) - watercraft , computer science , false alarm , wavelet , artificial intelligence , computer vision , wavelet transform , object detection , pattern recognition (psychology) , remote sensing , geology , oceanography
This work provides a new, to the best of our knowledge, approach to constructing linear models for object detection in a scene. Specifically, we use representative training data in order to estimate the parameters describing a generalized wavelet model for the express purpose of detecting the presence of maritime targets in a scene. The parameter estimates are taken as those that maximize the probability of detecting the targets for a fixed probability of false alarm. The approach is then demonstrated on a database of short-wave infrared imagery containing various watercraft. Results are then compared to some of the more standard wavelet bases used in detection applications.