ASSESSMENT OF THE MULTIPLE ENDMEMBER SPECTRAL MIXTURE ANALYSIS (MESMA) MODEL APPLIED TO THE HYPERION/EO-1 HYPERSPECTRAL DATA OF THE COASTAL PLAIN OF RIO GRANDE DO SUL, BRAZIL

摘要

The objective of this work was to evaluate the potential use of the Multiple Endmember Spectral Mixture Analysis (MESMA) when applied to EO-1 Hyperion hyperspectral data to discriminate land covers in the southern state of Rio Grande do Sul, Brazil. The methodology involved: (a) pre-processing and atmospheric correction of Hyperion data; (b) sequential use of the Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n-Dimensional Visualizer techniques in the 454-2334 nm range for the initial selection of a general group of endmember candidates (first spectral library) and of another group of pixels to be used for model validation; (c) use of the Visualization and Image Processing for Environmental Research Tools (VIPER Tools) to perform the final selection of endmembers based on the first spectral library and to obtain MESMA models; and (d) evaluation of resultant fraction images and root mean square error (RMSE) values to determine the optimal number of components of the MESMA model. Results showed that a four-endmember MESMA model (soil velence dunes and dry fields; green vegetation velence pinus, eucalyptus and grasslands; water velence without sediments, with sediments, and with chlorophyll; and shade) adequately described the diversity of the scene components, including that of materials within the same class (e.g., pinus and eucalyptus) and produced the largest fractions and the lowest RMSE values on a per-pixel basis. Results demonstrated the potential use of the MESMA with EO-1 Hyperion hyperspectral data to discriminate land covers in the coastal plains of Rio Grande do Sul, even considering the low signal-to-noise ratio of the instrument, especially in the shortwave infrared range.