Shrub of a thousand faces: an individual segmentation from satellite images using deep learning

Monitoring the distribution and size structure of long-living shrubs, such as Juniperus communis, can be used to estimate the long-term effects of climate change on high-mountain and high latitude ecosystems. Historical aerial very-high resolution imagery offers a retrospective tool to monitor shrub growth and distribution at high precision. Currently, deep learning models provide impressive results for detecting and delineating the contour of objects with defined shapes. However, adapting these models to detect natural objects that express complex growth patterns, such as junipers, is still a challenging task. This research presents a novel approach that leverages remotely sensed RGB imagery in conjunction with Mask R-CNN-based instance segmentation models to individually delineate Juniperus shrubs above the treeline in Sierra Nevada (Spain). In this study, we propose a new data construction design that consists in using photo interpreted (PI) and field work (FW) data to respectively develop and externally validate the model. We also propose a new shrub-tailored evaluation algorithm based on a new metric called Multiple Intersections over Ground Truth Area (MIoGTA) to assess and optimize the model shrub delineation performance. Finally, we deploy the developed model for the first time to generate a wall-to-wall map of Juniperus individuals. The experimental results demonstrate the efficiency of our dual data construction approach in overcoming the limitations associated with traditional field survey methods. They also highlight the robustness of MIoGTA metric in evaluating instance segmentation models on species with complex growth patterns showing more resilience against data annotation uncertainty. Furthermore, they show the effectiveness of employing Mask R-CNN with ResNet101-C4 backbone in delineating PI and FW shrubs, achieving an F1-score of 87,87% and 76.86%, respectively.

Deep Learning for blind spectral unmixing of LULC classes with MODIS multispectral time series and ancillary data

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.

What is the best RNN-cell structure for forecasting each time series behavior?

It is unquestionable that time series forecasting is of paramount importance in many fields. The most used machine learning models to address time series forecasting tasks are Recurrent Neural Networks (RNNs). Typically, those models are built using one of the three most popular cells, ELMAN, Long-Short Term Memory (LSTM), or Gated Recurrent Unit (GRU) cells, each cell has a different structure and implies a different computational cost. However, it is not clear why and when to use each RNN-cell structure. Actually, there is no comprehensive characterization of all the possible time series behaviors and no guidance on what RNN cell structure is the most suitable for each behavior. The objective of this study is two-fold: it presents a comprehensive taxonomy of all-time series behaviors (deterministic, random-walk, nonlinear, long-memory, and chaotic), and provides insights into the best RNN cell structure for each time series behavior. We conducted two experiments: (1) The first experiment evaluates and analyzes the role of each component in the LSTM-Vanilla cell by creating 11 variants based on one alteration in its basic architecture (removing, adding, or substituting one cell component). (2) The second experiment evaluates and analyzes the performance of 20 possible RNN-cell structures. Our results showed that the MGU-SLIM3 cell is the most recommended for deterministic and nonlinear behaviors, the MGU-SLIM2 cell is the most suitable for random-walk behavior, FB1 cell is advocated for long-memory behavior, and LSTM-SLIM1 for chaotic behavior.