Remote Sensing Course
Master the full remote sensing workflow, from sensor physics and image acquisition to classification, quantitative analysis, and real-world applications. This course covers optical, thermal, SAR, and LiDAR systems alongside cloud computing and machine learning methods. Develop the technical skills that environmental scientists, GIS analysts, and geospatial professionals rely upon every day.
What you will learn:
You will build a thorough understanding of how satellite and airborne sensors acquire data across the electromagnetic spectrum and how atmospheric and geometric distortions are corrected. You will learn to classify land cover using both traditional and machine learning approaches, assess classification accuracy, and extract quantitative biophysical parameters from imagery. The course also covers SAR interferometry, UAV data processing, and cloud-based analysis of large satellite archives. Applied project modules address vegetation monitoring, disaster assessment, urban mapping, and water resource analysis. By the end, you will be equipped to design and execute professional-grade remote sensing workflows for a wide range of scientific and applied objectives.
How you study in a practical way Remote Sensing Course
How you practise Remote Sensing Course
For companies looking to train their teams
With Dedika for businesses, the course includes exercises and examples tailored to your own business and the way your company needs.
Course content
8 Chapters • 40 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsFoundations of Remote Sensing
Foundations of Remote Sensing
Lesson 1 • Electromagnetic Spectrum Basics
Covers wavelength, frequency, and energy relationships across the EM spectrum. Provides the physical basis for understanding how sensors detect surface features.
Lesson 2 • Atmospheric Effects on Signals
Analyses scattering, absorption, and refraction caused by the atmosphere. Prepares students to recognise and correct atmospheric distortions in imagery.
Lesson 3 • Energy-Matter Interactions
Examines reflection, absorption, transmission, and emission at Earth surfaces. Links spectral behaviour to material identification in imagery.
Lesson 4 • History and Applications Overview
Traces remote sensing from aerial photography to modern satellite constellations. Contextualises current applications across agriculture, forestry, and urban mapping.
Lesson 5 • Remote Sensing System Components
Describes sensors, platforms, and data transmission chains. Connects hardware architecture to image quality and data availability.
Chapter 2HideHide detailsSee detailsSensor Types and Image Acquisition
Sensor Types and Image Acquisition
Lesson 1 • Orbital Parameters and Revisit Times
Explains sun-synchronous, geostationary, and low-Earth orbits and their trade-offs. Links orbital design to temporal resolution and coverage requirements.
Lesson 2 • LiDAR and Active Optical Systems
Examines laser-based ranging for 3D surface and canopy measurement. Demonstrates how point cloud density relates to terrain and vegetation mapping.
Lesson 3 • Passive Optical Sensors
Covers multispectral and hyperspectral sensor design and spectral resolution. Connects sensor specifications to land cover discrimination capability.
Lesson 4 • Thermal Infrared Sensors
Addresses emitted thermal radiation detection and land surface temperature retrieval. Connects thermal data to urban heat island and wildfire monitoring.
Lesson 5 • Active Microwave Sensors
Introduces radar and SAR systems that transmit and receive their own energy. Explains all-weather, day-night imaging advantages over passive systems.
Chapter 3HideHide detailsSee detailsDigital Image Fundamentals
Digital Image Fundamentals
Lesson 1 • Image File Formats and Metadata
Reviews common geospatial raster formats and embedded metadata standards. Prepares students to ingest, export, and share imagery across platforms.
Lesson 2 • Spatial Resolution and Scale
Analyses how ground sampling distance affects feature detectability and mapping accuracy. Guides resolution selection for specific application requirements.
Lesson 3 • Radiometric Properties of Images
Covers digital numbers, radiance, and reflectance and their conversion relationships. Enables consistent quantitative comparison across scenes and sensors.
Lesson 4 • Histogram Analysis and Statistics
Uses histograms and descriptive statistics to assess image quality and tonal distribution. Connects statistical summaries to preprocessing and enhancement decisions.
Lesson 5 • Raster Data Structure
Defines pixels, bands, bit depth, and spatial extent in raster datasets. Establishes the data model underlying all image processing operations.
Chapter 4HideHide detailsSee detailsImage Preprocessing and Correction
Image Preprocessing and Correction
Lesson 1 • Image Mosaicking and Compositing
Merges multiple scenes into seamless spatial and temporal composites. Addresses radiometric balancing and cloud masking across overlapping tiles.
Lesson 2 • Atmospheric Correction Methods
Applies image-based and model-driven methods to remove atmospheric path radiance. Produces surface reflectance products suitable for spectral analysis.
Lesson 3 • Resampling and Reprojection
Transforms imagery between coordinate systems and spatial resolutions. Evaluates resampling kernel effects on spectral and spatial data integrity.
Lesson 4 • Geometric Correction and Orthorectification
Removes sensor tilt, terrain displacement, and projection distortions from imagery. Aligns corrected images to reference maps for accurate spatial analysis.
Lesson 5 • Radiometric Calibration Techniques
Converts raw digital numbers to physically meaningful radiance values. Ensures sensor-to-sensor and scene-to-scene comparability for time-series work.
Chapter 5HideHide detailsSee detailsImage Enhancement and Visualisation
Image Enhancement and Visualisation
Lesson 1 • Spatial Filtering Operations
Uses convolution kernels for smoothing, sharpening, and edge detection. Connects filter selection to feature extraction and noise reduction goals.
Lesson 2 • Band Combinations and Colour Composites
Assigns spectral bands to RGB channels to highlight specific land cover types. Demonstrates how composite selection drives visual interpretation outcomes.
Lesson 3 • Image Fusion and Pan-Sharpening
Merges high-resolution panchromatic data with multispectral bands. Evaluates fusion quality using spatial and spectral fidelity metrics.
Lesson 4 • Spectral Indices and Transformations
Computes ratio-based indices and principal component transforms to isolate features. Links index selection to vegetation, water, and built-up area mapping.
Lesson 5 • Contrast Enhancement Techniques
Applies linear, histogram equalisation, and standard deviation stretches to imagery. Improves visual discrimination of features with similar tonal values.
Chapter 6HideHide detailsSee detailsImage Classification and Feature Extraction
Image Classification and Feature Extraction
Lesson 1 • Supervised Classification Workflow
Guides training sample collection, classifier training, and image labelling. Connects field knowledge to spectral signature definition and class separability.
Lesson 2 • Object-Based Image Analysis
Segments imagery into objects using spectral and spatial homogeneity criteria. Enables shape, texture, and context features beyond pixel-level classification.
Lesson 3 • Post-Classification Processing
Applies majority filtering, class merging, and change detection to classified maps. Refines thematic outputs for cartographic and analytical use.
Lesson 4 • Accuracy Assessment Methods
Constructs confusion matrices and computes overall, producer, and user accuracy. Validates classification reliability against independent reference data.
Lesson 5 • Unsupervised Classification Methods
Applies k-means and ISODATA clustering to group spectrally similar pixels. Introduces iterative clustering logic before supervised training concepts.
Chapter 7HideHide detailsSee detailsQuantitative Analysis and Spectral Modeling
Quantitative Analysis and Spectral Modeling
Lesson 1 • Vegetation Biophysical Parameter Retrieval
Retrieves LAI, canopy cover, and chlorophyll content from spectral data. Links retrieval accuracy to sensor spectral resolution and model assumptions.
Lesson 2 • Land Surface Temperature Retrieval
Converts thermal band radiance to land surface temperature using emissivity data. Supports urban heat, drought, and wildfire intensity assessments.
Lesson 3 • Regression-Based Estimation Models
Builds empirical and semi-empirical regression models linking spectral data to field measurements. Evaluates model transferability across sites and seasons.
Lesson 4 • SAR-Based Quantitative Retrievals
Extracts soil moisture, biomass, and surface roughness from SAR backscatter. Demonstrates radar-specific modelling approaches for non-optical retrievals.
Lesson 5 • Spectral Mixture Analysis
Decomposes mixed pixels into fractional abundances of pure endmembers. Addresses sub-pixel heterogeneity in coarse-resolution land cover mapping.
Chapter 8HideHide detailsSee detailsApplied Remote Sensing Projects
Applied Remote Sensing Projects
Lesson 1 • Land Use and Land Cover Mapping
Executes a full LULC mapping workflow from data acquisition to validated thematic map. Applies classification and accuracy assessment skills in a realistic project context.
Lesson 2 • Urban and Infrastructure Mapping
Maps impervious surfaces, building footprints, and urban expansion using high-resolution data. Supports urban planning and infrastructure assessment workflows.
Lesson 3 • Disaster Assessment and Response
Applies rapid image analysis for damage assessment after floods, fires, and earthquakes. Emphasises time-critical workflows and multi-sensor data fusion for emergency response.
Lesson 4 • Vegetation and Agricultural Monitoring
Monitors crop growth stages and biomass using time-series spectral indices. Connects phenological patterns to agricultural management decisions.
Lesson 5 • Water Resource and Coastal Monitoring
Delineates water bodies, monitors turbidity, and maps coastal change using optical and SAR data. Integrates multi-sensor approaches for comprehensive water resource assessment.
Your valid completion certificate
This course is for you:
Geography students: eager to move beyond theory into hands-on satellite analysis.
Environmental consultants: needing satellite-based evidence to support field assessments.
GIS technicians: ready to add image analysis capabilities to their existing spatial toolkit.
Ecology researchers: wanting to monitor landscapes and vegetation at regional scales.
Urban planners: seeking data-driven methods to track land use and city growth.
Career changers: transitioning into geospatial roles from adjacent science or engineering fields.
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