Law, Military Science and Security Sovereignty of the State

Active Sensor Image Analysis Techniques Training

SAQA US 244516 | NQF 5 | Credits 12 | Duration 9 Days
From $1,318 per delegate

Description

This course equips participants with the skills to analyse images acquired by active sensors, such as radar and LiDAR. Learners will interpret sensor data, apply processing techniques, and extract meaningful information for applications in remote sensing, GIS, and environmental monitoring.

Learning Outcomes

  • Apply principles of active sensor imaging to interpret acquired data.
  • Analyse image characteristics including resolution, noise, and geometric distortions.
  • Evaluate different processing techniques for enhancing active sensor imagery.
  • Extract thematic information from active sensor images using classification methods.
  • Demonstrate proficiency in using software tools for active sensor image analysis.
  • Validate analysis results through ground truthing and accuracy assessment.

Target Audience

This course is designed for GIS technicians, remote sensing analysts, and environmental scientists who work with active sensor imagery and need to enhance their analytical capabilities.

Prerequisites

None — open enrollment.

Course Outline

Day 1: Introduction to Active Sensors and Image Acquisition

Objectives:
• Understand the principles of active sensors (e.g., LiDAR, radar, sonar).
• Identify different types of active sensors and their applications.
• Describe the image acquisition process for active sensors.
• Explain the advantages and limitations of active versus passive sensors.

Topics:
• Overview of remote sensing: passive vs active sensors.
• Principles of LiDAR, radar, and sonar.
• Sensor components: transmitter, receiver, scanning mechanism.
• Imaging modes: strip mapping, spotlight, pushbroom.
• Factors affecting image quality: resolution, noise, geometry.
• Applications in environmental monitoring, defence, and engineering.

Day 2: Image Preprocessing and Calibration

Objectives:
• Apply radiometric calibration to correct sensor noise.
• Perform geometric correction to remove distortions.
• Understand speckle noise and filtering techniques.
• Implement basic image enhancement methods.

Topics:
• Radiometric calibration: gain, offset, atmospheric correction.
• Geometric correction: sensor model, ground control points, resampling.
• Speckle noise in SAR and LiDAR: causes and reduction.
• Image enhancement: contrast stretching, histogram equalization.
• Data formats and metadata extraction.

Day 3: Image Filtering and Feature Extraction

Objectives:
• Apply spatial filters for noise reduction and edge detection.
• Extract basic features: edges, corners, and textures.
• Understand frequency domain filtering.
• Use morphological operations for image analysis.

Topics:
• Spatial filtering: mean, median, Gaussian filters.
• Edge detection: Sobel, Canny, Roberts operators.
• Texture analysis: GLCM, Gabor filters.
• Frequency domain: Fourier transform, high-pass/low-pass filtering.
• Morphological operations: dilation, erosion, opening, closing.

Day 4: Image Segmentation and Object Detection

Objectives:
• Segment images using thresholding, clustering, and region growing.
• Detect objects using template matching and Hough transform.
• Evaluate segmentation accuracy.
• Apply segmentation to active sensor imagery.

Topics:
• Thresholding: global, adaptive, Otsu's method.
• Clustering: K-means, mean shift.
• Region-based segmentation: region growing, watershed.
• Object detection: template matching, Hough transform for lines and circles.
• Segmentation evaluation: Jaccard index, Dice coefficient.

Day 5: Classification Techniques for Active Sensor Data

Objectives:
• Perform supervised and unsupervised classification.
• Understand pixel-based vs object-based classification.
• Apply machine learning classifiers (e.g., SVM, random forest).
• Assess classification accuracy using confusion matrix.

Topics:
• Supervised classification: maximum likelihood, SVM, random forest.
• Unsupervised classification: K-means, ISODATA.
• Object-based image analysis: segmentation, rule-based classification.
• Accuracy assessment: confusion matrix, kappa coefficient.
• Handling mixed pixels and sub-pixel analysis.

Day 6: Advanced Techniques: Deep Learning for Active Sensor Imagery

Objectives:
• Understand CNN architectures for image analysis.
• Apply pre-trained models for feature extraction.
• Train a simple CNN for classification or segmentation.
• Evaluate deep learning models on active sensor data.

Topics:
• Introduction to neural networks and CNNs.
• Popular architectures: U-Net, ResNet, YOLO.
• Transfer learning and fine-tuning.
• Data augmentation for active sensor imagery.
• Training considerations: overfitting, batch normalization, dropout.

Day 7: 3D Point Cloud Processing from LiDAR and Radar

Objectives:
• Process LiDAR point clouds: filtering, segmentation, classification.
• Generate digital elevation models (DEM) and canopy height models.
• Understand radargrammetry and InSAR for 3D reconstruction.
• Extract geometric features from point clouds.

Topics:
• Point cloud filtering: ground vs non-ground, noise removal.
• Segmentation: region growing, RANSAC for plane detection.
• DEM generation: interpolation, gridding.
• LiDAR classification: vegetation, buildings, ground.
• Radar interferometry (InSAR): phase unwrapping, deformation mapping.

Day 8: Time-Series Analysis and Change Detection

Objectives:
• Perform change detection using multi-temporal active sensor data.
• Analyze time-series for environmental monitoring.
• Use coherence and amplitude-based methods for SAR change detection.
• Implement techniques for urban growth and deforestation detection.

Topics:
• Change detection methods: image differencing, ratioing, PCA.
• SAR coherence change detection.
• Time-series analysis: stack processing, temporal filtering.
• Applications: deforestation, urban expansion, crop monitoring.
• Accuracy assessment for change detection.

Day 9: Integration, Case Studies, and Future Trends

Objectives:
• Integrate active sensor analysis with GIS and other data sources.
• Review real-world case studies (e.g., flood mapping, infrastructure monitoring).
• Understand current challenges and future directions.
• Present a mini-project applying techniques from the course.

Topics:
• Integration with GIS: raster-vector overlay, spatial analysis.
• Multi-sensor data fusion: combining LiDAR, SAR, and optical.
• Case studies: flood mapping, mining, defence.
• Future trends: AI, real-time processing, small satellites.
• Mini-project presentations and peer feedback.

Practicals

27 hours of practicals To be conducted online or on-campus or in-house
Overview

Hands-on practicals are essential to develop proficiency in processing and analyzing active sensor imagery. Learners will use open-source software (e.g., SNAP, QGIS, Python) to apply preprocessing, classification, and 3D analysis techniques on real datasets.

Practical Activities
  • Practical 1: Preprocessing and Enhancement of SAR and LiDAR Data — Learners perform radiometric calibration, speckle filtering, and geometric correction on provided SAR and LiDAR datasets using SNAP and QGIS. (6h)
  • Practical 2: Segmentation and Object Detection — Using Python (OpenCV, scikit-image), learners segment images and detect objects (e.g., buildings, vehicles) in LiDAR and radar imagery. (6h)
  • Practical 3: Classification and Deep Learning — Learners perform supervised classification using SVM and random forest, then train a simple CNN for land cover classification on SAR data. (6h)
  • Practical 4: 3D Point Cloud Processing and DEM Generation — Using CloudCompare and Python, learners process LiDAR point clouds: filter ground points, segment objects, and generate DEMs. (6h)
  • Practical 5: Change Detection and Mini-Project — Learners apply change detection techniques on multi-temporal SAR data and present a mini-project integrating analysis from previous practicals. (3h)

Summatives

Each delegate is assessed continuously throughout the course via daily exercises, scored practical assignments, and a final summative test at the end.

Practical Assignments — 30%

Practical assignments are observed and scored against a rubric during the practical sessions. Each delegate's practical mark is averaged into a single 100% score and contributes 30% to the final total.

Daily Exercises — 20%

Every training day ends with a multiple-choice exercise scored out of 100%. The scores from each daily exercise are averaged across the duration of the course to produce a Daily Average mark, which contributes 20% to the final total.

Final Test — 50%

On the last day a final summative test is written. It is a multiple-choice paper with multiple-answer questions: each question may have more than one correct option, and a single wrong selection on a question marks the entire question wrong — no partial credit. The final test is scored out of 100% and contributes 50% to the overall mark.

Final Total
Component Out of Weight
Practical Assignments (rubric-scored) 100% 30%
Daily Average (multiple choice) 100% 20%
Final Test (multi-answer multiple choice) 100% 50%
Final Total 100%

All marks are recorded on the AATICD LMS and visible to each learner under their account.

Certificate

Certificate of Completion

Awarded to delegates who achieve an overall mark of 50% or higher on the Final Total (Practicals 30% + Daily Average 20% + Final Test 50%).

How it works
  • Certificates are auto-generated on the AATICD LMS as soon as the marks pass the 50% threshold.
  • Each certificate is a branded PDF with the delegate's name, the course title, the unit standard ID, NQF level, credits, and the date of issue.
  • You can download or print your certificate from your LMS dashboard at any time after issue — there's no reissue fee and no expiry date.
  • If you scored under 50% you can sit the final test again at the next scheduled session at no extra cost.
Where to find it

Sign in to the LMS, open your dashboard, and your certificates appear under My Certificates. Each entry has a View / Download button and a print option.

Training Discounts

Group discounts apply automatically — the more delegates you enrol, the greater the saving. Discounts are calculated at 3% per 5 delegates, scaling up to 40% off for 100+ delegates.

Delegates Discount
5 3% off
10 6% off
15 9% off
20 12% off
25 15% off
30 18% off
50 30% off
75 35% off
100 40% off

3% discount per 5 delegates, up to 40% off for 100+ delegates. Contact us for a custom group quote.

Upcoming Training Sessions
Online training — attend live sessions from anywhere via our virtual classroom.
Start End Delivery Season Price Action
Fri 11 Sep 2026 Wed 23 Sep 2026 Virtual Spring 2026 $1,318 Register
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Tue 28 Dec 2027 Fri 07 Jan 2028 Virtual Summer 2027 $1,318 Register
Mon 17 Jan 2028 Thu 27 Jan 2028 Virtual Summer 2027 $1,318 Register
On-Campus training — face-to-face sessions at our training venues across Africa and beyond.
Showing all 485 sessions across 26 venues
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Wed 20 Jan 2027 Mon 01 Feb 2027 Your Premises Summer 2026 $1,712 Register
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Thu 25 Mar 2027 Tue 06 Apr 2027 Your Premises Autumn 2027 $1,712 Register
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Wed 27 Oct 2027 Mon 08 Nov 2027 Your Premises Spring 2027 $1,712 Register
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Wed 05 Jan 2028 Mon 17 Jan 2028 Your Premises Summer 2027 $1,712 Register
Tue 25 Jan 2028 Fri 04 Feb 2028 Your Premises Summer 2027 $1,712 Register
Training Discounts
Delegates Discount
5 3% off
10 6% off
15 9% off
20 12% off
25 15% off
30 18% off
50 30% off
75 35% off
100 40% off

3% off per 5 delegates, up to 40% for 100+

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