Transformation of Landsat imagery into pseudo-hyperspectral imagery by a multiple regression-based model with application to metal deposit-related minerals mapping

摘要

Hyperspectral remote sensing is superior to traditional multispectral remote sensing in detailed spectral information but has limited spatial and temporal coverage. Those limitations require an innovative technique that can simulate hyperspectral imagery from multispectral imagery with global coverage, continuous acquisition, and a small number of bands. For this, a combination of Hyperion and Landsat 7 ETM+ images is a representative target. The present study develops a new method, Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), for transforming Landsat 7 ETM+ imagery into pseudo-Hyperion imagery using correlations between Landsat and Hyperion band reflectance data. Each correlation is defined as a multiple linear regression model selected through Bayesian model averaging, in which Hyperion and Landsat bands are dependent and predictor variables, respectively. The resultant pseudo-image has a number of high-quality Hyperion bands of the same scene size as a Landsat image. Through verification of transformation accuracy by statistical analyses and surface mineral mapping, the pseudo-Hyperion image was proven very similar to the original band reflectances, because of large Pearson's correlation coefficients (generally > 0.94), small RMS error (mostly < 0.016), high structural similarity, and similar appearance of the color composite image. Using a reference mineral map built from an AVIRIS image and field surveys as ground truth, an advantage of the pseudo-image is clarified for the Cuprite hydrothermal alteration area in the western United States. The identification and mapping accuracies of metal deposit-related minerals were high even in areas outside the original Hyperion scene. Featured absorptions were reconstructed in pseudo-reflectance spectra of the typical minerals in the area. The results can enhance the potential of a large Landsat-series dataset over the long term by transformation into pseudo-Hyperion images for global land surfaces. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.