Module 1: Introduction to Digital Image Processing for Classification and Segmentation

• Overview of digital imagery and its characteristics
• Applications of image classification and segmentation
• Pixels vs. Objects: different approaches to image analysis
• Workflow for image classification and segmentation projects
• Introduction to common software platforms (e.g., ENVI, ArcGIS Pro, QGIS, Google Earth Engine)
• Practical session: Navigating and exploring various types of digital imagery in a GIS/RS software

Module 2: Image Pre-processing for Classification and Segmentation

• Radiometric calibration and atmospheric correction
• Geometric correction and image registration
• Image enhancement techniques (e.g., contrast stretching, filtering)
• Data reduction: band selection and principal component analysis (PCA)
• Handling noise and missing data (e.g., clouds, shadows)
• Practical session: Applying radiometric and geometric corrections to satellite images

Module 3: Supervised Classification Techniques

• Principles of supervised classification
• Training data collection and selection (Region of Interest - ROI creation)
• Parametric classifiers: Maximum Likelihood Classification (MLC)
• Non-parametric classifiers: Support Vector Machines (SVM), Decision Trees
• Feature space analysis and separability measures
• Practical session: Performing a supervised classification using Maximum Likelihood

Module 4: Unsupervised Classification Techniques

• Principles of unsupervised classification
• K-Means clustering algorithm
• ISODATA clustering algorithm
• Determining the optimal number of clusters
• Interpreting and refining unsupervised classification results
• Practical session: Conducting an unsupervised classification using ISODATA and K-Means

Module 5: Object-Based Image Analysis (OBIA) and Segmentation Fundamentals

• Introduction to Object-Based Image Analysis (OBIA)
• Advantages of OBIA over pixel-based methods
• Principles of image segmentation: homogeneity and heterogeneity
• Multi-resolution segmentation algorithm
• Scale parameter optimization for segmentation
• Practical session: Performing multi-resolution segmentation on an image

Module 6: Advanced Segmentation Techniques

• Region merging and splitting algorithms
• Edge detection and region growing methods
• Watershed segmentation
• Contextual and hierarchical segmentation approaches
• Using multiple features for segmentation (spectral, spatial, textural)
• Practical session: Experimenting with different segmentation parameters and algorithms

Module 7: Feature Extraction for Classification and Segmentation

• Spectral features (e.g., band values, vegetation indices)
• Spatial features (e.g., texture, shape, size)
• Textural features (e.g., GLCM - Gray Level Co-occurrence Matrix)
• Contextual features (e.g., neighborhood relationships)
• Deriving features for object-based classification
• Practical session: Extracting textural features from imagery and preparing them for classification

Module 8: Accuracy Assessment and Validation of Classified and Segmented Images

• Principles of accuracy assessment
• Generating random validation points
• Creating confusion matrices and deriving accuracy metrics (e.g., overall accuracy, Kappa coefficient, producer's, user's accuracy)
• Sources of error and uncertainty in classification
• Post-classification refinement techniques (e.g., majority filter, sieve analysis)
• Practical session: Conducting a comprehensive accuracy assessment of a classified image

Module 9: Machine Learning and Deep Learning for Image Classification and Segmentation

• Introduction to machine learning algorithms (Random Forest, Gradient Boosting)
• Concepts of deep learning for image analysis (CNNs - Convolutional Neural Networks)
• Training data considerations for deep learning
• Semantic segmentation vs. instance segmentation
• Using pre-trained models and transfer learning
• Practical session: Applying a machine learning classifier (e.g., Random Forest) for image classification

Module 10: Applications and Case Studies in Digital Image Classification and Segmentation

• Land cover/land use mapping and monitoring
• Urban area analysis and change detection
• Agricultural crop identification and health monitoring
• Environmental habitat mapping and conservation
• Disaster damage assessment (e.g., floods, fires)
• Practical session: Working through an end-to-end case study applying learned techniques to a specific application area

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588