Supervised Image Classification Algorithms

Dear Colleague,

In the last few decades, the scientific community, government agencies, private industry, the general public, and many others have been using information provided by airborne and satellite remote sensing data. Particularly, in the last few years, this use has faced an exponential growth due to the emergence of new (high resolution) platforms, the availability of free data and, especially, the addition of new variables to this equation, such as Volunteered Geographic Information (VGI), Big Data and Unmanned Aerial Vehicles (UAV). This lead to the development and/or the rebirth of new classification methodologies and algorithms, using segmentation, GEOgraphic Object Based Image Analysis (GEOBIA), Fuzzy analysis, Spectral Unmixing and time-series analysis, just to name a few. This leads to the co-existence of two paradigms: The pixel-based approaches and multi-scale object-based approaches. Even in the pixel-based paradigm, one can see some emerging trend like the use of Machine Learning algorithms, such as random forests, supported vector machines, Neural Networks and Genetic algorithms and, at the same time, very traditional approaches, such as maximum likelihood classification. Thus, this is the time to perform a survey of the emerging methods and algorithms used in supervised image classification and their application to the different sciences and thematic fields.

Dr. Jorge Rocha
Guest Editor

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• Object Based Image Analysis (GEOBIA)
• Volunteer Geographic Information (VGI)
• Big Data
• Unmanned Aerial Vehicles (UAV)
• High resolution images
• Airborne Laser Scanner 
• Hyperspectral Imagery
• Fuzzy analysis
• Spectral Unmixing
• Time-series analysis
• Machine Learning
• Random forests
• Supported vector machines
• Neural Networks
• Genetic algorithms