Next Generation Kernel-Based Machine Learning for Big Missing Data Applied to Earth Observation

Informations

Funding country

Norway

Acronym

-

URL

-

Start date

1/1/2015

End date

12/31/2019

Budget

1,016,349 EUR

Fundings

Abstract

In today's society, data is gathered at an incredible speed mainly because of massive sensory monitoring and logging of processes, and abundant and inexpensive storage. Machine learning is the state-of-the-art scientific field for revealing patterns in big data for making data-driven decisions, forming the backbone of technology such as face detection in digital cameras, recommender systems (Amazon, Facebook etc.), machine translation, and speech recognition, to name a few, and is extremely important in areas like health and medicine, neuroscience, and satellite based monitoring. However, low quality of data in the form of incomplete recordings, known as missing data, severely limits the power of machine learning algorithms. This frequently leads to inferior decision-making. This project shall develop the next generation machine learning algorithms, with the power to handle missing data. This will leap forward decision making from big data and the field of information and communication technologies. The project has resulted in a range of publications. Specifically, one article that will be mentioned here is the paper "Time series cluster kernel for learning similarities between multivariate time series with missing data" by Mikalsen, Jenssen et al., published in the journal Pattern Recognition in 2018. The method is generic, built on so-called "kernel methods" and applicable for all types of multivariate time series data, and handles missing data in a more efficient manner compared to traditional techniques. In addition, the article "Urban Land Cover Classification With Missing Data Modalities Using Deep Convolutional Neural Networks" by Kampffmeyer, Salberg, Jenssen in the journal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing in 2018, is mentioned here. This method develops so-called deep learning in order to monitor the earth's surface using satellite images, where some of the spectral bands are missing completely or partially. This type of research has been further extended, for instance in the paper "A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images" av Liu, Salberg, Jenssen i International Geoscience and Remote Sensing Symposium (IGARSS) 2019. These works are building on a range of other publications within the project, and have pushed the research frontier forward.