  Identifying and preserving archaeological sites before they are destroyed is
a very important issue. In this paper, we develop a greatly improved
archaeological predictive model $APM_{enhanced}$ that predicts where
archaeological sites will be found. This approach is applied to remotely-sensed
multispectral bands and a single topographical band obtained from advanced
remote sensing technologies such as satellites and Airborne Laser Scanning
(ALS). Our $APM_{enhanced}$ is composed of band transformation, image analysis,
feature extraction and classification. We evaluate our methodology on the
sensor bands over Ft.Irwin, CA, USA. A nested bi-loop cross-validation and
receiver operating characteristics curves are used to assess the performance of
our algorithm. We first validate our method on the east swath of Ft.Irwin and
then test on a separate dataset from the west swath of Ft.Irwin. A convex
combination of two methodologies: $APM_{conventional}$, which has been used
among archaeologists for many years, and our $APM_{enhanced}$, is demonstrated
to yield superior classification performance compared to either alone at low
false negative rates. We compare the performance of our methodology on
different band combinations, chosen based on the archaeological importance for
these sensor bands. We also compare the two types of $APM$s in the aspects of
input data, output values, practicality and transferability.
