Remotely sensed data are dominated by mixed Land Use and Land Cover (LULC) types. Spectral unmixing is a technique to extract information from mixed pixels into their constituent LULC types and corresponding abundance fractions. Traditionally, solving this task has relied on either classical methods that require prior knowledge of endmembers or machine learning methods that avoid explicit endmembers calculation, also known as blind spectral unmixing (BSU). Most BSU studies based on Deep Learning (DL) focus on one time-step hyperspectral data, yet its acquisition remains quite costly compared with multispectral data. To our knowledge, here we provide the first study on BSU of LULC classes using multispectral time series data with DL models. We further boost the performance of a Long-Short Term Memory (LSTM)-based model by incorporating geographic plus topographic (geo-topographic) and climatic ancillary information. Our experiments show that combining spectral-temporal input data together with geo-topographic and climatic information substantially improves the abundance estimation of LULC classes in mixed pixels. To carry out this study, we built a new labeled dataset of the region of Andalusia (Spain) with monthly multispectral time series of pixels for the year 2013 from MODIS at 460m resolution, for two hierarchical levels of LULC classes, named Andalusia MultiSpectral MultiTemporal Unmixing (Andalusia-MSMTU). This dataset provides, at the pixel level, a multispectral time series plus ancillary information annotated with the abundance of each LULC class inside each pixel. The dataset and code are available to the public.

翻译：遥感数据中混合土地利用与土地覆盖（LULC）类型占主导地位。光谱解混是从混合像元中提取其组成LULC类型及对应丰度分数的技术。传统上，解决该问题依赖两类方法：需要先验端元知识的经典方法，或无需显式端元计算的机器学习方法（即盲光谱解混）。基于深度学习（DL）的盲光谱解混研究多集中于单时相高光谱数据，但其采集成本远高于多光谱数据。据我们所知，本文首次提出利用多光谱时间序列数据与DL模型进行LULC类别盲光谱解混。通过融合地理与地形（地理-地形）及气候辅助信息，我们进一步提升了基于长短期记忆（LSTM）模型的性能。实验表明，将光谱-时间输入数据与地理-地形及气候信息相结合，显著改善了混合像元中LULC类别的丰度估计。为开展本研究，我们构建了西班牙安达卢西亚地区的新标注数据集，包含2013年MODIS 460米分辨率下的月度多光谱时间序列像元，覆盖两个层级的LULC类别体系，命名为安达卢西亚多光谱多时相解混数据集（Andalusia-MSMTU）。该数据集提供像素级多光谱时间序列及辅助信息，并标注各像元内LULC类别的丰度。数据集与代码已公开。