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

• URL: http://arxiv.org/abs/2310.07223v3
• Date: Wed, 24 Jan 2024 08:11:02 GMT
• Title: Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data
• Authors: Jos\'e Rodr\'iguez-Ortega (1 and 2), Rohaifa Khaldi (2), Domingo Alcaraz-Segura (3), Siham Tabik (1) ((1) Department of Computer Science and Artificial Intelligence, DaSCI, University of Granada, Granada, Spain, (2) LifeWatch-ERIC ICT Core, Seville, Spain, (3) Department of Botany, Faculty of Science, University of Granada, Granada, Spain)
• Abstract summary: Spectral unmixing (SU) is a technique that disentangles mixed pixels into constituent Land Use and Land Cover (LULC) types. Our work pioneers SU using MODIS MS time series, addressing missing data with end-to-end DL models. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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