Advanced Techniques in Remote Sensing
( 24 Modules )

Module #1
Introduction to Advanced Remote Sensing
Overview of remote sensing, its applications, and the importance of advanced techniques

Module #2
Remote Sensing Data Sources and Characteristics
Exploration of different remote sensing data sources, including satellite, aerial, and UAV platforms

Module #3
Pre-processing of Remote Sensing Data
Techniques for radiometric, geometric, and atmospheric corrections of remote sensing data

Module #4
Object-Based Image Analysis
Introduction to object-based image analysis and its applications in remote sensing

Module #5
Supervised Classification Techniques
Overview of supervised classification techniques, including maximum likelihood, decision trees, and random forests

Module #6
Unsupervised Classification Techniques
Introduction to unsupervised classification techniques, including clustering and K-means

Module #7
Change Detection and Monitoring
Techniques for detecting and monitoring changes using remote sensing data

Module #8
Land Cover Classification and Mapping
Best practices for land cover classification and mapping using remote sensing data

Module #9
Object-Based Change Detection
Advanced techniques for object-based change detection and monitoring

Module #10
Hyperspectral Remote Sensing
Introduction to hyperspectral remote sensing, its applications, and analysis techniques

Module #11
LiDAR Remote Sensing
Introduction to LiDAR remote sensing, its applications, and analysis techniques

Module #12
Fusion of Multi-Sensor Data
Techniques for fusing data from different remote sensing platforms and sensors

Module #13
Machine Learning and Deep Learning in Remote Sensing
Introduction to machine learning and deep learning techniques for remote sensing data analysis

Module #14
Image Segmentation and Feature Extraction
Techniques for image segmentation and feature extraction in remote sensing

Module #15
Accuracy Assessment and Validation
Best practices for assessing and validating the accuracy of remote sensing results

Module #16
Big Data Analytics in Remote Sensing
Introduction to big data analytics and its applications in remote sensing

Module #17
Cloud Computing and Remote Sensing
Introduction to cloud computing and its applications in remote sensing

Module #18
Open-Source Remote Sensing Tools and Software
Overview of open-source remote sensing tools and software, including Google Earth Engine and QGIS

Module #19
Remote Sensing for Environmental Monitoring
Applications of remote sensing for environmental monitoring, including climate change and natural disasters

Module #20
Remote Sensing for Urban Planning and Development
Applications of remote sensing for urban planning and development, including infrastructure monitoring and urban sprawl

Module #21
Remote Sensing for Agricultural Monitoring
Applications of remote sensing for agricultural monitoring, including crop yield prediction and soil moisture analysis

Module #22
Remote Sensing for Disaster Response and Recovery
Applications of remote sensing for disaster response and recovery, including damage assessment and infrastructure planning

Module #23
Remote Sensing for Forestry and Ecological Monitoring
Applications of remote sensing for forestry and ecological monitoring, including deforestation and habitat fragmentation

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Advanced Techniques in Remote Sensing career

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Advanced Techniques in Remote Sensing ( 24 Modules )

Advanced Techniques in Remote Sensing
( 24 Modules )