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Endmember variability based abundance estimation of red and black soil over sparsely vegetated area using AVIRIS-NG hyperspectral image

Sahadevan, AS and Ahmad, T and Lyngdoh, RB and Kumar, DN (2023) Endmember variability based abundance estimation of red and black soil over sparsely vegetated area using AVIRIS-NG hyperspectral image. In: Advances in Space Research .

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Visible-near-and-short-wave-infrared hyperspectral images (HSI) have proven helpful for mapping the soil types over bare soils pixels. However, the accuracy of the traditional pixel-based classification methods decreases due to the spectral mixing between the significant agricultural features such as vegetation, soil and crop residue. In this context, spectral unmixing algorithms are handy for estimating the sub-pixel abundances of soil over sparsely vegetated areas. Most of the spectral unmixing methods focus on analyzing the HSI by considering the pixels as independent entities. However, in the HSIs of agricultural fields, the endmembers are spatially and temporally variable. It is also known that there exists a strong spatial correlation among neighbourhood pixels over agricultural fields. Therefore, both endmember variability and the neighbourhood spatial-spectral information are critical to estimate soil abundance accurately. Here we address the issue of estimating the abundances of two major soil types with spectral variability. The proposed approach combines the endmember bundle extraction with the spectral-spatial weighted unmixing approach (SSWU-SV). Experimental analysis has been carried out on the airborne HSI acquired by airborne-visible-and-infrared-imaging- spectrometer-next-generation (AVIRIS-NG) sensor. The quantitative analysis reveals that the proposed method consistently achieves a better unmixing performance than the traditional linear mixing model in terms of spectral angle mapper (SAM), and root-mean-square error (RMSE). Our results also indicate the saturation of normalized difference vegetation index (NDVI) at high vegetative fractions obtained from SSWU-SV.

Item Type: Journal Article
Publication: Advances in Space Research
Publisher: Elsevier Ltd
Additional Information: The copyright of this article belongs to Elsevier Ltd.
Keywords: Hyperspectral Image (HSI); AVIRIS-NG; Red soil; Black soil; Sparse unmixing;
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Division of Mechanical Sciences > Civil Engineering
Date Deposited: 14 Jun 2023 12:27
Last Modified: 14 Jun 2023 12:27
URI: https://eprints.iisc.ac.in/id/eprint/82014

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