Feature-Based Methods for Remote Sensing Image Classification

Dear Colleagues,

Remote sensing (RS) currently occupies a privileged position in Earth observation (EO)-based studies and applications, as well as in Earth data science, allowing the monitoring, understanding and modelling of our environment and its evolution at a global scale and with an unprecedented spatio-temporal resolution. EO data have been employed to monitor croplands and forested areas, oceans and seas, urban settlements, mountainous areas or climate-related processes and natural hazards. In all these cases, the extraction of features and information from EO data, either qualitative or quantitative, as well as their temporal dynamics is an essential step to better characterizing and understanding these different environments and processes, their interactions, and to also disentangle anthropogenic effects.

For instance, land-cover classes and land-cover changes at the global scale can be regularly updated thanks to automatic classification methods that are applicable to several EO data sources, including multi-spectral, hyperspectral and synthetic aperture radar (SAR) images. In this context, the accurate extraction of features and the estimation of essential variables from EO data, their classification and analysis are research topics of high interest to the scientific community, especially when considering global applications. Indeed, the transfer of locally trained classification techniques or the generalization of classification methodologies to a global scale remain open questions. Nevertheless, novel and highly relevant questions are currently emerging in these domains thanks to new technological advances. New developments in EO capabilities provided by new satellite constellations or improved imaging technologies, or developments in classification based on machine learning or deep learning, could allow precise classification on a large scale and with an unprecedented temporal sampling. In addition, the availability of different EO data sources with high spatio-temporal resolutions may allow access to novel features, not accessible in low or medium resolution data, and trigger the development of new feature extraction techniques to better understand the data information content. 

The main objective of this Special Issue is to address these emerging topics in feature-based methods for RS data processing and classification by gathering contributions on novel feature extraction techniques, advanced classification methodologies, but also algorithms able to improve and increase the understanding of EO-based variables derived from large and various RS imagery collections.

Dr. Carlos López-Martínez
Dr. Ramona-Maria Pelich
Dr. Minh-Tan Pham
Guest Editors

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