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Shadow-aware nonlinear spectral unmixing with spatial regularization

Zhang, Guichen and Scheunders, Paul and Cerra, Daniele (2023) Shadow-aware nonlinear spectral unmixing with spatial regularization. IEEE Transactions on Geoscience and Remote Sensing, 61, p. 5517516. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2023.3289570. ISSN 0196-2892.

[img] PDF - Postprint version (accepted manuscript)

Official URL: https://ieeexplore.ieee.org/document/10163827


Current shadow-aware hyperspectral unmixing (HySU) methods often suffer from noisy abundance maps and inaccurate abundance estimation of shadowed pixels, as these are characterized by low reflectance values and signal-to-noise ratio. In order to achieve a shadow-insensitive abundance estimation, in this article, we propose a novel spatial–spectral shadow-aware mixing (S3AM) model. The approach models shadows by considering diffuse solar illumination and secondary illumination from neighboring pixels. Besides, spatial regularization using shadow-aware weighted total variation (TV) is employed. Specifically, pixels in the local neighborhood of a target pixel simultaneously consider spectral similarity measures derived from the imagery, elevation similarity measures derived from a digital surface model (DSM), and the impact of shadows. The sky view factor F , needed as input for the model, is also derived from available DSMs. The proposed approach is extensively validated and compared with state-of-the-art methods on two datasets. Results demonstrate that the S3AM yields superior abundance estimation maps for real scenarios, by decreasing the noise in the results and achieving more accurate reconstructions in the presence of shadows.

Item URL in elib:https://elib.dlr.de/195732/
Document Type:Article
Title:Shadow-aware nonlinear spectral unmixing with spatial regularization
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Cerra, DanieleUNSPECIFIEDhttps://orcid.org/0000-0003-2984-8315UNSPECIFIED
Date:26 June 2023
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:p. 5517516
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:spectral unmixing spectral mixing model shadow-aware spatial regularization total variation digital surface model
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Optical remote sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zhang, Guichen
Deposited On:07 Jul 2023 09:25
Last Modified:19 Oct 2023 09:57

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