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

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.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Deep Learning for Hyperspectral Image Classification: An Overview
• More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
• Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning
• Deep Hyperspectral Unmixing using Transformer Network
• Predicting missing values in spatio-temporal satellite data
• Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing
• Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey
• Illumination invariant hyperspectral image unmixing based on a digital surface model
• TIML: Task-Informed Meta-Learning for Agriculture