Training Course on Machine Learning for Remote Sensing - Python, Remote Sensing Software

Module 1: Introduction to Machine Learning in Remote Sensing

Theory:

Overview of machine learning: supervised, unsupervised, and semi-supervised methods.

Key applications in remote sensing: classification, regression, anomaly detection, object detection.

Introduction to Python libraries for ML: scikit-learn, TensorFlow, Keras, NumPy, pandas, matplotlib.

Applications: Urban planning, environmental monitoring, agriculture, disaster response.

Hands-on: Set up Python environment, load and visualize sample satellite imagery, explore basic data structures.

Case Study: Classify land cover types using Landsat imagery.

Module 2: Remote Sensing Data Acquisition and Preprocessing

Theory:

Types of remote sensing data: multispectral, hyperspectral, UAV imagery, LiDAR.

Radiometric, geometric, and atmospheric corrections.

Noise reduction and normalization.

Methods: Using Python and remote sensing software for preprocessing workflows.

Hands-on: Preprocess Sentinel-2 and Landsat datasets, resample images, handle missing data.

Case Study: Preparing vegetation datasets for machine learning classification.

Module 3: Feature Extraction and Engineering

Theory:

Spectral indices (NDVI, NDWI, SAVI, EVI) and their applications.

Texture analysis, morphological features, and topographic indices.

Dimensionality reduction: PCA and LDA.

Applications: Crop classification, forest mapping, urban analysis.

Hands-on: Compute spectral indices, extract texture features, apply PCA for feature reduction.

Case Study: Feature extraction for detecting crop health variations.

Module 4: Supervised Classification Techniques

Theory:

Supervised classifiers: Random Forest, Support Vector Machines, k-Nearest Neighbors, Neural Networks.

Training and validation dataset preparation.

Evaluation metrics: accuracy, confusion matrix, Kappa coefficient.

Applications: Land cover mapping, urban area monitoring, habitat classification.

Hands-on: Train a Random Forest classifier on satellite imagery, evaluate model accuracy, tune hyperparameters.

Case Study: Mapping urban expansion using Sentinel-2 imagery.

Module 5: Unsupervised Classification and Clustering

Theory:

K-means, ISODATA, hierarchical clustering for remote sensing data.

Applications: exploratory analysis, anomaly detection, land cover segmentation.

Applications: Forest type classification, water body detection.

Hands-on: Apply K-means clustering on multi-spectral imagery, visualize clusters.

Case Study: Forest vegetation type classification using unsupervised methods.

Module 6: Change Detection and Temporal Analysis

Theory:

Post-classification comparison, image differencing, PCA-based change detection.

Temporal analysis using multi-date imagery.

Applications: Deforestation monitoring, urban growth detection, disaster assessment.

Hands-on: Detect changes over time using Sentinel-2 multi-temporal images, generate change maps and statistics.

Case Study: Deforestation detection in tropical forest areas.

Module 7: Advanced Machine Learning Techniques

Theory:

Deep learning for remote sensing: Convolutional Neural Networks (CNNs) for image classification.

Transfer learning with pre-trained models.

Ensemble learning for improved accuracy.

Applications: Object detection, road extraction, crop type mapping.

Hands-on: Train a simple CNN for land cover classification, implement an ensemble Random Forest + SVM classifier.

Case Study: Detecting water bodies and built-up areas using CNNs.

Module 8: Model Evaluation, Validation, and Optimization

Theory:

Cross-validation techniques.

Confusion matrix, precision, recall, F1-score.

Hyperparameter tuning and model optimization.

Applications: Improving classification accuracy, validating crop or forest maps.

Hands-on: Evaluate classifier performance, adjust hyperparameters, generate validation reports.

Case Study: Accuracy assessment for crop type classification in agricultural regions.

Module 9: Automation and Integration with GIS Workflows

Theory:

Automating ML workflows using Python scripts and batch processing.

Integration with GIS software for visualization, reporting, and spatial analysis.

Applications: Large-scale land cover mapping, automated disaster monitoring.

Hands-on: Automate classification of multiple satellite images, export results to GIS formats.

Case Study: Monitoring urban growth or vegetation changes over large areas using automated ML workflows.

Module 10: Capstone Project

Project Options:

Land cover classification and change detection using multi-temporal imagery.

Crop health monitoring and yield prediction using NDVI and machine learning.

Urban sprawl detection and predictive modeling using supervised ML.

Hands-on: Complete end-to-end workflow: preprocessing → feature extraction → model training → classification → evaluation → visualization.

Presentation: Demonstrate results, explain methodology, and discuss real-world implications.