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.

通过深度学习模型，结合光谱 - 时间序列数据和地理 - 地形和气候信息，提高多光谱遥感数据中土地利用和土地覆盖类型的成分估计，该研究还提供了一个新的标记数据集和代码。