Spectral unmixing of Landsat Thematic Mapper data

Abstract

Abstract The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel. Despite this knowledge most image classification techniques aim at labelling a pixel according to a singular surface category. An alternative product can be generated using spectral unmixing: a technique that strives to find the surface abundances of a number of spectral components together causing the observed spectral reflectance at a pixel. A stepwise approach to implement spectral unmixing in Landsat Thematic Mapper image analysis is proposed: (1) atmospheric calibration of the image data, (2) preselection of a large number of ‘candidate’ endmembers, (3) reduction to the most important spectral endmembers using spectral angle mapping, (4) finding the relative abundances of the endmembers through spectral unmixing analysis, (5) combining the abundance estimates into a final product comparable to a classified image, and (6) accuracy assessment. A Landsat Thematic Mapper image from southern Spain covering a large peridotite body with adjacent limestone and low-grade metamorphic rocks is used as an example to demonstrate the usefulness of unmixing.