利用 MODIS 多光谱时间序列以及地理地形和气候数据进行 LULC 类别的双向循环插补与丰度估计
Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data
AI总结：
提出融合 MODIS 多光谱时间序列与地理地形、气候辅助信息的端到端 LSTM 解混框架，并发布安达卢西亚像元级丰度标注数据集。
摘要：
遥感数据以混合的土地利用与土地覆盖（LULC）类型为主。光谱解混（SU）是一项关键技术，可将混合像元分解为其组成的 LULC 类型及其丰度比例。尽管现有关于深度学习（DL）用于 SU 的研究通常聚焦于单个时间步的高光谱（HS）或多光谱（MS）数据，但我们的工作开创性地使用 MODIS MS 时间序列进行 SU，并通过端到端 DL 模型处理缺失数据。我们的方法通过纳入地理、地形（geo-topographic）和气候辅助信息，增强了一个基于长短期记忆（LSTM）的模型。值得注意的是，该方法无需显式提取端元，而是通过监督学习学习混合光谱与 LULC 丰度之间的输入—输出关系。实验结果表明，将光谱—时间输入数据与地理地形和气候信息相结合，可显著改善混合像元中 LULC 丰度的估计。为推动本研究，我们整理了一个面向西班牙安达卢西亚的新型标注数据集，其中包含 2013 年、460 米分辨率的月度 MODIS 多光谱时间序列。该数据集命名为 Andalusia MultiSpectral MultiTemporal Unmixing（Andalusia-MSMTU），提供 LULC 丰度的像元级标注以及辅助信息。数据集（https://zenodo.org/records/7752348）和代码（https://github.com/jrodriguezortega/MSMTU）均向公众开放。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
