Delineation of sub-pixel level sedimentary litho-contacts by super resolution mapping of Landsat TM image

Abstract

To delineate the geological formation at the surface, satellite image classification approaches are often preferred. This study aims to produce a super resolved map with better delineation of the litho-contacts from the medium resolution Landsat image. Conventionally used per-pixel classification provides an output map at the same resolution of the satellite image, while the super resolved map provides the high resolution output map using the medium resolution image. In this study, four test sites are considered for delineating different litho-contacts using super resolution mapping approach in Cuddalore district, southern India. The test sites consists of charnockite, fissile hornblende-biotite gneiss, marine sandstone and sandstone with clay, limestone with calcareous shale and clay, clay with limestone bands/lenses, mio-pliocene and quaternary argillaceous and calcareous sandstone, fluvial and fluviomarine formations. This work compares the per-pixel, super resolved output derived from linear spectral unmixing (LSU) based HNN and spectral angle mapper (SAM) based HNN approaches. The super resolution mapping approach was performed on the medium resolution (30m) Landsat image to obtain the litho-contact maps and the results are compared with the existing maps and observations from field visits. The results showed improved accuracy (90.92%) of the map prepared by the SAM based super resolution approach compared to the LSU based super resolution approach (90.14%) and the maximum likelihood classification approach (83.74%). Such an improved accuracy of the super resolved map (6m resolution) is due to the fact that the lithological mapping is done not merely at the resolution of the image, but at the sub-pixel level. Hence, it is inferred that super resolution mapping applied to multispectral images may be preferred for mapping lithounits and litho-contacts than the conventional per-pixel and sub-pixel image classification methods.