Remote Sensing Image Acquisition, Analysis and Applications

This module introduces the course, covering the fundamental nature of remote sensing, platforms and sensor types used. It includes lectures on what remote sensing is, the atmosphere, platforms used for imaging the earth's surface, and how images are recorded.

This module covers what remote sensing aims to measure, distortions in recorded images, geometric distortion, and methods for correcting geometric distortion.

This module focuses on correcting geometric distortion using mapping functions and control points, resampling, image registration, image interpretation and use, and enhancing image contrast.

This module introduces classification (quantitative analysis), correlation and covariance, and the principal components transform.

This module provides worked examples of the principal components transform, its applications, and includes a test on Module 1 content.

This module introduces fundamentals of image analysis and machine learning, covering the maximum likelihood classifier and the minimum distance classifier.

This module focuses on training linear classifiers and the support vector machine, including training, classification steps, and handling non-linear and multiple class data.

This module covers neural networks as classifiers, including training and examples.

This module introduces deep learning and convolutional neural networks (CNNs), including examples in remote sensing and comparisons of different classifiers.

This module covers unsupervised classification and clustering, including k-means clustering, other clustering methods, and clustering for big data. It also includes a test on Module 2 content.

This module focuses on feature reduction, exploiting the structure of the covariance matrix, feature reduction by transformation, and separability measures.

This module covers assessing classifier performance and map errors, choosing testing pixels for accuracy assessment, classification methodologies, and other interpretation methods.

This module introduces radar imaging fundamentals, synthetic aperture radar (SAR), scattering coefficients, and speckle in radar imagery.

This module covers radar scattering from the earth's surface, sub-surface imaging, volume scattering, scattering from hard targets, and scattering from the sea.

This module covers geometric distortions in radar imagery, radar interferometry, other considerations in radar remote sensing, and concludes the course. It includes a test on Module 3 content.