Advanced Mathematical Methods in Remote Sensing

Dear Colleagues,

Remote sensing is part of Earth observation and is now used in all spheres of human society to monitor and quickly obtain a large amount of data over a large area (phenomena and objects in this area). With the technological development of humankind, computers are also becoming more advanced, and their use is increasing. These advances make it possible to perform more complex mathematical operations, such as processing multispectral and hyperspectral images (a larger number of images of the same area taken at different wavelengths) using machine learning methods and, in particular, deep learning. Advances in computer technology have led to the rapid development of artificial intelligence methods and their application in many areas of human activity. Today, artificial intelligence can be implemented in the cloud computing environment, offering more flexibility, agility, and cost savings by hosting data and applications. Very sophisticated and large-scale data processing tasks can be performed remotely without the need for specialized and expensive mainframe computers. Affordable compact industrial computers are quite sufficient to perform the complex mathematical operations required.

Mathematical methods and algorithms are the basis of many conventional and modern tools used in remote sensing, especially digital image processing and spatial data analysis and processing.

The goal of this Special Issue is to attract and publish manuscripts that present advanced developed mathematical models and algorithms that are applied within remote sensing methods. This mainly refers to the processing of digital images, sensor fusion, big data analysis, and the visualization of the results of remote sensing methods. Contributions on modern uses of mathematical methods and an algorithm for processing a large number of images and data are especially invited. This refers mostly to machine and deep learning methods in image processing and big data and artificial intelligence methods in remote sensing.

Dr. Andrija Krtalić
Dr. Marko Horvat
Dr. Amila Akagic
Guest Editors

Manuscript Submission Information

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