GI25405 Hyperspectral and Thermal Remote Sensing – Semester IV – Geo – R-2025

Subject Code & Name: GI25405 – Hyperspectral and Thermal Remote Sensing

Regulation: R-2025

Semester: IV (Fourth Semester)

Branch: B.E. Geoinformatics Engineering (Geo)

Credits / L-T-P: 3 Credits | L-T-P: 2-0-2

Course Objectives

• To impart comprehensive knowledge on the principles of hyperspectral and thermal remote sensing, including image characteristics, sensor systems, and data acquisition processes.

Full Unit-wise Syllabus

Unit I – Fundamentals of Hyperspectral Remote Sensing

Diffraction principles; imaging spectrometry; spectral reflectance, BDRF and field spectroscopy; hyperspectral sensors – characteristics and applications; image cube concept, spectral dimensionality and Hughes phenomenon; calibration and normalization; red-edge concept; spectral libraries and response functions.

Practical: 1. Hyperspectral data visualization and spectral profile analysis with basic preprocessing.

Unit II – Hyperspectral Data Processing and Analysis

Preprocessing of hyperspectral data – atmospheric correction and noise removal; MNF transformation; PCA and WPCA; band selection techniques; spectral angle mapper (SAM); library matching; spectral mixture analysis; endmember extraction; linear mixture models; dimensionality reduction; overview of software tools such as ENVI, ERDAS and processing using Python/MATLAB.

Practical: 2. Dimensionality reduction and classification of hyperspectral imagery.

Unit III – Fundamentals of Thermal Remote Sensing

Thermal infrared radiation principles and radiation laws; thermal properties of terrain; emissivity; thermal sensors and image characteristics; data acquisition methods; environmental considerations; image degradation and correction techniques.

Practical: 3. Thermal image calibration and emissivity mapping.

Unit IV – Thermal Data Analysis and Applications

Land Surface Temperature (LST) retrieval; thermal brightness temperature conversion; evapotranspiration estimation; soil moisture assessment; urban heat island analysis; applications in agriculture, geology and water resources; integration of hyperspectral and thermal datasets for thematic mapping.

Practical: 4. Land Surface Temperature (LST) estimation and hotspot analysis.

Tasks

• Perform hyperspectral data preprocessing, dimensionality reduction, and classification for application in mineral exploration and vegetation health assessment.
• Process thermal imagery to estimate Land Surface Temperature (LST) and analyze hotspots for application in urban heat island and water resource studies.
• Integrate hyperspectral and thermal datasets for feature extraction and thematic mapping for application in precision agriculture and environmental monitoring.

Course Outcomes (COs)

• CO1: Explain principles of hyperspectral and thermal remote sensing including image formation and sensor characteristics.
• CO2: Apply preprocessing and dimensionality reduction techniques using standard tools.
• CO3: Analyze data using classification, spectral unmixing, and temperature retrieval methods.
• CO4: Evaluate and integrate datasets for environmental and geospatial applications.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50% Methodology for Continuous Assessment : Quiz (5%), Project (15%), Assignment Programs (25%), Practical (25%), Internal Examinations (30%)

Source: Official Anna University – B.E. Geoinformatics Engineering R-2025 Syllabus
Last Updated: October 2026