Course Details

Satellite Image Processing with ERDAS Imagine Training Course

Introduction

Satellite imagery provides an unparalleled perspective of our planet, offering critical data for a vast range of applications, from environmental monitoring and urban planning to disaster management and resource exploration. However, raw satellite images often contain distortions, atmospheric interference, and require specific enhancements to reveal their full informational content. Satellite Image Processing is the crucial set of techniques used to transform this raw data into meaningful and actionable information. ERDAS Imagine is one of the industry-leading geospatial image processing software suites, renowned for its comprehensive capabilities in remote sensing, photogrammetry, and GIS integration. It provides a robust environment for correcting image defects, enhancing visual interpretation, extracting features, and performing advanced analyses on satellite and aerial imagery. Mastering ERDAS Imagine empowers users to unlock the full potential of remotely sensed data, enabling precise mapping, accurate change detection, and in-depth environmental analysis. Without proficiency in such a powerful processing tool, professionals often struggle to extract reliable information from their imagery, leading to suboptimal decision-making and inefficient workflows. Many aspiring remote sensing specialists and GIS professionals find the array of tools and complex workflows within advanced image processing software daunting without structured, hands-on training.

Conversely, a solid command of ERDAS Imagine for satellite image processing equips professionals with the ability to perform complex radiometric, geometric, and atmospheric corrections, generate accurate land cover classifications, detect subtle changes over time, and derive advanced insights from various types of imagery. This specialized skill set is crucial for transforming raw satellite data into high-value, actionable geographic intelligence for a multitude of scientific, governmental, and commercial applications. Our intensive 5-day "Satellite Image Processing with ERDAS Imagine" training course is meticulously designed to equip GIS professionals, remote sensing analysts, environmental scientists, urban planners, geologists, agriculturalists, and researchers with the essential theoretical knowledge and practical, hands-on skills required to confidently process, analyze, and interpret satellite imagery using ERDAS Imagine.

Duration

5 Days

Target Audience

The "Satellite Image Processing with ERDAS Imagine" training course is ideal for a wide range of professionals and researchers who need to process, analyze, and extract information from satellite and aerial imagery. This includes:

• Remote Sensing Analysts: Seeking to master ERDAS Imagine for advanced image processing.
• GIS Professionals: Who work with raster data and want to enhance their image analysis skills.
• Environmental Scientists and Ecologists: For land cover mapping, change detection, and environmental monitoring.
• Urban Planners and Developers: For urban growth analysis, land use mapping, and site suitability.
• Agriculturalists and Agronomists: Utilizing imagery for crop health, yield estimation, and precision farming.
• Foresters and Natural Resource Managers: For forest inventory, deforestation monitoring, and habitat assessment.
• Geologists and Mineral Exploration Specialists: For geological mapping and structural analysis.
• Students and Academics: In geography, remote sensing, environmental science, and related fields.
• Anyone involved in projects that require extracting valuable information from satellite or aerial imagery.

Course Objectives

Upon successful completion of the "Satellite Image Processing with ERDAS Imagine" training course, participants will be able to:

• Understand the fundamental concepts of digital image processing for remote sensing data.
• Navigate and effectively utilize the ERDAS Imagine interface and its core tools.
• Perform various radiometric and atmospheric corrections on satellite imagery.
• Apply geometric correction techniques, including image-to-map and image-to-image registration.
• Execute different image enhancement operations to improve visual interpretation.
• Conduct both supervised and unsupervised image classification for land cover mapping.
• Perform basic change detection analysis using multi-temporal imagery.
• Generate and export high-quality image products and integrate them with GIS.

 Course Modules

Module 1: Introduction to ERDAS Imagine and Remote Sensing Basics

• Overview of remote sensing principles and the electromagnetic spectrum.
• Introduction to ERDAS Imagine: Interface, toolboxes, and data types (raster, vector, pyramids).
• Opening, displaying, and navigating satellite images within ERDAS Imagine.
• Understanding image properties: Bands, pixels, resolution (spatial, spectral, radiometric, temporal).
• Basic image visualization techniques: True color, false color composites.

Module 2: Radiometric and Atmospheric Correction

• Understanding radiometric errors and their sources (e.g., sensor calibration, atmospheric effects).
• Radiometric Calibration: Converting Digital Numbers (DNs) to radiance and reflectance.
• Atmospheric Correction: Methods to remove atmospheric haze and scattering effects (e.g., Dark Object Subtraction, ATCOR, FLAASH - conceptual).
• Techniques for histogram adjustment and contrast enhancement.
• Image statistics and histogram analysis.

Module 3: Geometric Correction and Image Registration

• Understanding geometric distortions in satellite imagery.
• Image-to-Map Georeferencing: Rectifying images to a map coordinate system using Ground Control Points (GCPs).
• Collecting and managing GCPs: Sources, distribution, and accuracy assessment (RMS error).
• Image-to-Image Registration: Aligning multiple images from different dates or sensors.
• Resampling methods: Nearest Neighbor, Bilinear Interpolation, Cubic Convolution and their impacts.

Module 4: Image Enhancement Techniques

• Contrast Enhancement: Linear stretch, histogram equalization, Gaussian stretch.
• Spatial Filtering: Applying convolution filters for smoothing (low-pass) and edge detection (high-pass).
• Band Combinations and Indices: Creating custom band combinations for specific applications (e.g., vegetation, urban areas).
• Deriving vegetation indices (e.g., NDVI, NDWI) and their interpretation.
• Principal Component Analysis (PCA) for data reduction and feature enhancement.

Module 5: Supervised Image Classification

• Introduction to image classification concepts: Training data, classifier algorithms, output.
• Supervised Classification Workflow: Defining classes, creating training signatures.
• Signature editor: Examining, merging, and evaluating training signatures.
• Common supervised algorithms: Maximum Likelihood Classifier, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN).
• Generating classified land cover maps from satellite imagery.

Module 6: Unsupervised Image Classification and Post-Classification Processing

• Unsupervised Classification Workflow: Clustering algorithms (e.g., ISODATA, K-Means).
• Interpreting and assigning classes to unsupervised clusters.
• Comparing supervised vs. unsupervised classification strengths and weaknesses.
• Post-Classification Processing: Majority filtering, clump and eliminate, sifting.
• Converting raster classes to vector polygons.

Module 7: Change Detection Analysis

• Methods for detecting land cover change using multi-temporal imagery.
• Image Differencing/Ratioing: Simple pixel-based change detection.
• Post-Classification Change Detection: Comparing two classified images.
• Generating change matrices and quantifying areas of gain/loss for specific land cover types.
• Visualizing and interpreting change detection results.

Module 8: Accuracy Assessment and Integration with GIS

• Importance of accuracy assessment for image classification and change detection.
• Collecting reference (ground truth) data for accuracy assessment.
• Confusion Matrix (Error Matrix): Calculating producer's accuracy, user's accuracy, overall accuracy, and Kappa coefficient.
• Integrating processed imagery and derived products (e.g., LULC maps) with GIS software (e.g., ArcGIS, QGIS).
• Exporting results for reporting and further spatial analysis.

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com