Geoscience-aware deep learning:A new paradigm for remote sensing

Information extraction is a key activity for remote sensing images. A common distinction exists between knowledge-driven and data-driven methods. Knowledge-driven methods have advanced reasoning ability and interpretability, but have difficulty in handling complicated tasks since prior knowledge is usually limited when facing the highly complex spatial patterns and geoscience phenomena found in reality. Data-driven models, especially those emerging in machine learning (ML) and deep learning (DL), have achieved substantial progress in geoscience and remote sensing applications. Although DL models have powerful feature learning and representation capabilities, traditional DL has inherent problems including working as a black box and generally requiring a large number of labeled training data. The focus of this paper is on methods that integrate domain knowledge, such as geoscience knowledge and geoscience features (GK/GFs), into the design of DL models. The paper introduces the new paradigm of geoscience-aware deep learning (GADL), in which GK/GFs and DL models are combined deeply to extract information from remote sensing data. It first provides a comprehensive summary of GK/GFs used in GADL, which forms the basis for subsequent integration of GK/GFs with DL models. This is followed by a taxonomy of approaches for integrating GK/GFs with DL models. Several approaches are detailed using illustrative examples. Challenges and research prospects in GADL are then discussed. Developing more novel and advanced methods in GADL is expected to become the prevailing trend in advancing remotely sensed information extraction in the future.</p

Expert Knowledge-Based Method for Satellite Image Time Series Analysis and Interpretation

International audienceFor many remote-sensing applications, there is usually a gap between the automatic analysis techniques and the direct expert interpretation. This semantic gap is all the more critical as the amount and diversity of satellite data increase. In this context, an important challenge is the integration of expert knowledge in automatic satellite image time series (SITS) analysis to improve results' reliability and precision. In this paper, we propose an original expert knowledge-based SITS analysis technique for land-cover monitoring and region dynamics assessing. Particularly, we are interested in extracting region temporal evolution similar to a given scenario proposed by the user, which can be useful in many applications such as urbanization and forest regions' monitoring. As a first step, with the formalization and exploitation of the expert semantic information, we construct a multitemporal knowledge base describing the remote-sensing scene ontology. Then, the temporal evolution of each region in the SITS is modeled by means of graph theory. Finally, given a user scenario, the most similar region temporal evolution is recognized using the marginalized graph kernel (MGK) similarity measure

Expert Knowledge-Based Method for Satellite Image Time Series Analysis and Interpretation

International audience<p>For many remote-sensing applications, there is usually a gap between the automatic analysis techniques and the direct expert interpretation. This semantic gap is all the more critical as the amount and diversity of satellite data increase. In this context, an important challenge is the integration of expert knowledge in automatic satellite image time series (SITS) analysis to improve results' reliability and precision. In this paper, we propose an original expert knowledge-based SITS analysis technique for land-cover monitoring and region dynamics assessing. Particularly, we are interested in extracting region temporal evolution similar to a given scenario proposed by the user, which can be useful in many applications such as urbanization and forest regions' monitoring. As a first step, with the formalization and exploitation of the expert semantic information, we construct a multitemporal knowledge base describing the remote-sensing scene ontology. Then, the temporal evolution of each region in the SITS is modeled by means of graph theory. Finally, given a user scenario, the most similar region temporal evolution is recognized using the marginalized graph kernel (MGK) similarity measure.</p