Advanced Concepts for Intelligent Vision Systems
Sept. 24-27, 2018
Espace Mendes France, Poitiers, France
Acivs 2018 Abstracts
Paper 196: Can we classify the world? Where Deep Learning Meets Remote Sensing
Deep learning has been recently gaining significant attention for the analysis of data in multiple domains. It seeks to model high-level knowledge as a hierarchy of concepts. With the exploding amount of available data, the improvement of hardware and the advances in training methodologies, now such hierarchies can contain many more processing layers than before, hence the adoption of the term "deep".
In remote sensing, recent years have witnessed a remarkable increase in the amount of available data, due to a consistent improvement in the spectral, spatial and temporal resolutions of the sensors. Moreover, there are new sources of large-scale open access imagery, governments are releasing their geographic data to the public, and collaborative platforms are producing impressive amounts of cartography. With such an overwhelming amount of information, it is of paramount importance to develop smart systems that are able to handle and analyze these data. The scalability of deep learning and its ability to gain insight from large-scale datasets, makes it particularly interesting to the remote sensing community. It is often the case, however, that the deep learning advances in other domains cannot be directly applied to remote sensing. The type of input data and the constraints of remote sensing problems require the design of specific deep learning techniques.
In this talk, I will discuss how deep learning approaches help in remote sensing image interpretation. In particular, I will focus on the most powerful architectures for semantic labeling of aerial and satellite optical images, with the final purpose to produce and update world maps.
Paper 197: Earth Observation Big Data Intelligence: the paradigm shift
Earth Observation (EO) Data Intelligence is addressing the entire value chain: data processing to extract information, the information analysis to gather knowledge, and knowledge transformation in value. The tutorial brings a joint understanding of the Artificila Intelligence (AI) and the of the Deep Learning methods indicating integrated optimal solutions in complex EO applications, including the choice or generation of labeled data sets and the biases influence in validation or benchmarking. EO starts with the mission intelligence, therefore focusing on the latent parametrs hidden in the process of physical parameters retrieval like, orbit, illumination, or imaging process. In this context specifc AI for EO methods are addressed in the tutorial for the practical cases of multisensor data fusion and Satellite Image Time Series analytics. The methods are exemplified with actual use cases for multispectral and Synthetic Aperture (SAR) images.