Have you ever wondered how you make satellite images clearer? Depending on the purpose, there are a variety of processes that allow you to extract the information you are looking for. This course offers a deep dive into the many techniques that are used, from the acquisition to the enhancement and interpretation of the images. You begin by learning about spatial filtering, band rationing, the principal component analysis and the Fourier Transformation Technique and their applications. In addition, you will explore the difference between supervised and unsupervised image classification, including their advantages and limitations, and be shown how to apply convolution filtering and density slicing classification on images using different software.

The course further discusses the mechanisms and application of the Light Detection and Ranging (LiDAR) technique in remote sensing. You will then learn about the application of the mosaicking technique, image subset and sub-sampling in digital image processing. Following on from this, gain insight into the causes of False Topographic Perception Phenomena (FTPP) on satellite images and the various ways they can be corrected. Lastly, you will examine the pros and cons of high spatial resolution satellite images and various image compression techniques.

If you are interested in the field of remote sensing, or have ever been curious about how satellite images are filtered and enhanced to improve their visualisation, this course is especially for you. Going into the details and technicalities, this course will upgrade your knowledge of every part of the topic. Not only will you find out where and how to get the raw data you need, but you will also be shown the software that can be used to process it. Enrich your expertise in satellite imagery with this course - enrol now to get started!