Advanced remote sensing

Module title:

Advanced remote sensing

Module overview:

This optional module introduces students to advanced algorithms for remote sensing data analysis and processing based on machine learning methods. Following the basic methods of remote sensing and processing of raster data types, this module will extend the material to point clouds and their processing. The main objectives of the module are:
Become familiar with different machine learning algorithms used for the data analysis of remote sensing.
Become familiar with different tools and approaches for the implementation of machine learning methods in the analysis of remote sensing data.
Become familiar with the methods of preparing remote sensing data for use in machine learning methods.
Become familiar with the point cloud generation methods.
Become familiar with the methods of classification of point clouds.
Become familiar with the methods of visualization of massive point clouds.
Become familiar with examples of projects that use machine learning and remote sensing methods.

The module requires previous basic experience in programming and knowledge of the subject of remote sensing in an expanded form. It is taught in selected software packages and selected development environments with the purpose of gaining practical experience. Evaluation of the module is based on solving a series of practical tasks.
In this module students will learn:
features and benefits of basic machine learning methods in remote sensing.
features and benefits of advanced machine learning methods in remote sensing.
how to integrate machine learning methods depending on the type of remote sensing and the information that needs to be created.
what preparatory actions need to be performed on the data of remote sensing to make them suitable for the application of machine learning methods.
what is the point cloud and how can it be generated.
how to classify a point cloud.
how to visualize massive point cloud.
how to use available tools for the processing and analysis of remote sensing data.

Literature:

Required readings:
1. Liang, S., Li, X., Wang, J. (2019) Advanced remote sensing: terrestrial information extraction and applications. 1st edn. Cambridge: Academic Press.

Supplementary readings:
1. Chang, N., Bai, K. (2018) Multisensor data fusion and machine learning for environmental remote sensing. Boca Raton: CRC Press.
2. Thenkabail, P. S., Lyon, J. G., Huete, A. (2019) Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation. 2nd edn. Boca Raton: CRC Press.