Linear models for SWIR surface spectra from the ECOSTRESS library

Germano S. Fonseca; Augusto Cezar G. dos Santos; Leonardo B. de Sá; José Gabriel R. C. Gomes

doi:10.1117/12.2587752

12 April 2021 Linear models for SWIR surface spectra from the ECOSTRESS library

Germano S. Fonseca, Augusto Cezar G. dos Santos, Leonardo B. de Sá, José Gabriel R. C. Gomes

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 117271J (2021) https://doi.org/10.1117/12.2587752
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

Hyperspectral imaging systems act as imaging spectrometers, acquiring dozens or hundreds of equally spaced spectral channels, thus leading to high complexity setups, low acquisition speed, and a large amount of data. In order to optimize the number of acquisition channels, and to mitigate these problems in shortwave infrared (SWIR) hyperspectral imaging systems, one must extend to the SWIR range the analysis and characterization methods that are available, in the literature, for the visible spectrum. To that end, this work focuses on the SWIR surface spectral reflectance (SSR) of possible objects that may be present in the scene, by analyzing an empirical SSR library that includes the SWIR range, as the ECOSTRESS spectral library. To the best of our knowledge, this is the first report of SWIR SSR data analysis in this library. The main goal of this paper is to investigate the approximation of data samples in this library by two linear models, namely truncated Fourier Series and principal components, both with less than a dozen basis vectors. This corresponds to significant dimension reduction in comparison to the number of acquisition channels, which lies in the several hundreds in the ECOSTRESS library. To validate the analysis and assess the quality of the reconstructed spectra, root mean squared error and goodness-of-fit coefficient(GFC) metrics are applied. An `accurate' to `excellent' fit with GFC median ranging between 0.995 and 0.9999 is obtained when reconstructing the signals with three to five principal components, and greater than 0.995 with three to five Fourier series terms.

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Citation Download Citation

Germano S. Fonseca, Augusto Cezar G. dos Santos, Leonardo B. de Sá, and José Gabriel R. C. Gomes "Linear models for SWIR surface spectra from the ECOSTRESS library", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 117271J (12 April 2021); https://doi.org/10.1117/12.2587752

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