Foundations of Remote Sensing Imagery

This unit establishes core concepts and practical skills for digital image and spatial data processing in remote sensing, including image representation, display, enhancement, arithmetic, filtering (spatial and frequency), color and data-fusion transformations, basic statistical methods, sensor types, and data/software resources. It prepares students to perform image registration, preprocessing, and build reproducible workflows that feed into advanced classification, change detection, and geospatial analysis in subsequent units.

Learning Objectives

• Demonstrate foundational knowledge of digital imagery by explaining image file structure, display attributes, histograms, and lookup tables, and by performing linear contrast enhancement on sample remote sensing data
• Apply pixel-wise arithmetic and data-fusion techniques—including image addition/subtraction, RGB–IHS transformation, and principal component analysis—to prepare multispectral datasets for interpretation and further analysis
• Analyze and implement spatial and frequency-domain filtering methods (convolution, Fourier-based filtering) and phase-correlation registration to correct geometric deformation and enhance feature extraction
• Evaluate available sensors, data sources, software tools, and spatial data formats, and assemble a general image-processing strategy and workflow for thematic mapping and change-detection case studies