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Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data

arXiv 2310.07223 ↗First posted 11 Oct 2023cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on Deep Learning (DL) for SU typically focus on single time-step hyperspectral (HS) or multispectral (MS) data, our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. Our approach enhances a Long-Short Term Memory (LSTM)-based model by incorporating geographic, topographic (geo-topographic), and climatic ancillary information. Notably, our method eliminates the need for explicit endmember extraction, instead learning the input-output relationship between mixed spectra and LULC abundances through supervised learning. Experimental results demonstrate that integrating spectral-temporal input data with geo-topographic and climatic information significantly improves the estimation of LULC abundances in mixed pixels. To facilitate this study, we curated a novel labeled dataset for Andalusia (Spain) with monthly MODIS multispectral time series at 460m resolution for 2013. Named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU), this dataset provides pixel-level annotations of LULC abundances along with ancillary information. The dataset (https://zenodo.org/records/7752348) and code (https://github.com/jrodriguezortega/MSMTU) are available to the public.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 88 references that are in the map. First posted on arXiv 11 Oct 2023.

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17 of 88 references.

References · 17

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5 more
  1. 2022Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Reviewcited by 13
  2. 2022TIML: Task-Informed Meta-Learning for Agriculturecited by 7
  3. 2021Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixingcited by 6
  4. 2020Illumination invariant hyperspectral image unmixing based on a digital surface modelcited by 4
  5. 2021Extending the Unmixing methods to Multispectral Imagescited by 1

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