  Conventional manual lithological mapping (MLM) through field surveys are
resource-extensive and time-consuming. Digital lithological mapping (DLM),
harnessing remotely sensed spectral imaging techniques, provides an effective
strategy to streamline target locations for MLM or an efficient alternative to
MLM. DLM relies on laboratory-generated generic end-member signatures of
minerals for spectral analysis. Thus, the accuracy of DLM may be limited due to
the presence of site-specific impurities. A strategy, based on a hybrid
machine-learning and signal-processing algorithm, is proposed in this paper to
tackle this problem of site-specific impurities. In addition, a soil pixel
alignment strategy is proposed here to visualize the relative purity of the
target minerals. The proposed methodologies are validated via case studies for
mapping of Limestone deposits in Jaffna, Ilmenite deposits in Pulmoddai and
Mannar, and Montmorillonite deposits in Murunkan, Sri Lanka. The results of
satellite-based spectral imaging analysis were corroborated with X-ray
diffraction (XRD) and Magnetic Separation (MS) analysis of soil samples
collected from those sites via field surveys. There exists a good
correspondence between the relative availability of the minerals with the XRD
and MS results. In particular, correlation coefficients of 0.8115 and 0.9853
were found for the sites in Pulmoddai and Jaffna respectively.
