Fundamentals of Remote Sensing Imagery

This unit introduces the core concepts, sensors, and preprocessing techniques used in remote sensing image processing and basic GIS spatial data handling. Students learn digital image structure, enhancement, filtering, color-space transformations, data-fusion and registration methods and develop an end-to-end workflow to prepare imagery for advanced analysis and classification in subsequent units.

Learning Objectives

• Analyze the structure and properties of digital images and compare major remote sensing sensor types (multispectral, hyperspectral, thermal, and SAR) to select appropriate data for mission objectives.
• Apply histogram modification, lookup tables, linear contrast enhancement, image addition and subtraction, and spatial/frequency-domain filtering (including convolution concepts) to preprocess imagery and improve feature visibility.
• Demonstrate color-coordinate transformations, RGB-IHS fusion techniques, and principal component analysis to perform multispectral data fusion and dimensionality reduction for downstream interpretation.
• Evaluate geometric deformation and image-registration methods (including phase correlation and basic radar interferometry principles), integrate spatial data operations and available software/data resources, and design a general image-processing workflow for thematic extraction case studies.