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Positive and unlabelled (PU) learning for multi-variate time series classification refers to build a binary classification model when only a small set of positive and a large set of unlabelled samples are accessible at training stage. Different from binary semi-supervised scenario in which the training set contains labelled samples from both positive and negative classes, in the PU learning setting, only positive samples are labelled due to cost-restriction or issues related to defining what belongs to the negative class. With the objective to deal with this challenging task, here, we propose a new deep learning framework, referred as DMTS-PUL. Our method has two different steps: firstly, it selects a set of reliable negative samples from the set of unlabelled data and, successively, it iteratively enriches the training data by selecting pseudo-labels to train a binary classification model via self-training. Experimental evaluations on several benchmarks have highlighted the quality of DMTS-PUL w.r.t. competing approaches and the obtained findings have pointed out the suitability of our proposal when only small amounts of positive labelled samples are available.

Standard supervised classification methods make the assumption that the training data is fully annotated thus requiring an a-priory labelling process which is both costly and time-consuming. To relax this requirement, many different flavors of weakly supervised learning have been proposed. Among weakly supervised learning strategies, Positive Unlabelled learning (PUL) is gaining attention from the research community due to the wide spectrum of applications it can fit. However, the majority of research studies related to PUL only consider binary classification tasks while real-world applications commonly involve multiple categories. To deal with this limitation, Multi-Positive Unlabelled learning (MPUL) has been recently introduced to learn from examples labelled with multiple positive labels and a single unknown negative label. Up to today, only a limited number of research works were proposed to cope with this more general setting. In this paper, we propose a new MPUL framework based on deep learning strategies. Our framework, named ProtoMPUL (Prototype based Multi-Positive and Unlabelled Learning), combines metric learning and clustering strategies to model the set of positive classes as well as to characterize the unknown negative one. Experimental evaluations on real-world benchmarks considering recent MPUL competitors demonstrates that the proposed framework achieves state-of-the-art performances, thus supporting the validity of the proposed approach.

Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the spatial patterns that characterize these data structures. Focusing on land cover classification (or mapping) tasks, the majority of approaches dealing with SITS data only considers the temporal dimension, while the integration of the spatial context is frequently neglected. In this work, we propose an attentive spatial temporal graph convolutional neural network that exploits both spatial and temporal dimensions in SITS. Despite the fact that this neural network model is well suited to deal with spatio-temporal information, this is the first work that considers it for the analysis of SITS data. Experiments are conducted on two study areas characterized by different land cover landscapes and real-world operational constraints (i.e., limited labeled data due to acquisition costs). The results show that our model consistently outperforms all the competing methods obtaining a performance gain, in terms of F-Measure, of at least 5 points with respect to the best competing approaches on both benchmarks.

The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich image data. One of the main tasks associated to SITS data analysis is related to land cover mapping. Due to operational constraints, the collected label information is often limited in volume and obtained at coarse granularity level carrying out inexact and weak knowledge that can affect the whole process. To cope with such issues, in the context of object-based SITS land cover mapping, we propose a new deep learning framework, named TASSEL (aTtentive weAkly Supervised Satellite image time sEries cLassifier), to deal with the weak supervision provided by the coarse granularity labels. Our framework exploits the multifaceted information conveyed by the object-based representation considering object components instead of aggregated object statistics. Furthermore, our framework also produces an additional outcome that supports the model interpretability. Quantitative and qualitative experimental evaluations are carried out on two real-world scenarios. Results indicate that not only TASSEL outperforms the competing approaches in terms of predictive performances, but it also produces valuable extra information that can be practically exploited to interpret model decisions.