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Satellite Remote Sensing Data Bootcamp With Opensource Tools
Master satellite remote sensing with open-source tools. Learn data analysis, preprocessing, and classification techniques.
Course Cost
₹ 2,699
Intermediate
Skill Level
5 Hours
Self-paced lessons
Dive into the world of satellite remote sensing with this comprehensive bootcamp using open-source tools. Starting with the basics, you'll explore various remote sensing data types and essential analysis tools like R and QGIS. The course covers optical remote sensing, including Landsat data handling and preprocessing techniques. You'll learn advanced topics such as texture indices, tasseled cap transformations, and dimension reduction using tools like GRASS GIS and ESA SNAP. The bootcamp also covers both supervised and unsupervised classification methods, integrating machine learning techniques. Finally, you'll explore Synthetic Aperture Radar (SAR) data, learning to preprocess and interpret active remote sensing information. This course is perfect for geospatial professionals, environmental scientists, and data analysts looking to enhance their remote sensing skills.
English
What you'll learn
Analyze different types of satellite remote sensing data
Preprocess optical data using atmospheric correction techniques
Classify remote sensing data using supervised and unsupervised methods
Handle SAR data, including preprocessing and speckle filtering
Apply band math and derive various indices from optical data
Use machine learning techniques for remote sensing classification
Work with open-source tools like QGIS, R, GRASS GIS, and ESA SNAP
Skills you'll gain
This course includes:
35 Minutes PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 6 modules in this course
This bootcamp offers a comprehensive introduction to satellite remote sensing data analysis using open-source tools. Students will learn about different types of remote sensing data, focusing on optical and Synthetic Aperture Radar (SAR) data. The course covers essential preprocessing techniques, including atmospheric correction and speckle filtering. Participants will explore various data analysis methods, such as band manipulation, vegetation indices, and dimension reduction. The curriculum also includes both supervised and unsupervised classification techniques, integrating machine learning concepts. By the end of the course, students will have hands-on experience with tools like QGIS, R, GRASS GIS, and ESA SNAP, enabling them to process, analyze, and interpret satellite remote sensing data effectively.
Introduction to Satellite Remote Sensing Data Analysis
Module 1 · 54 Minutes to complete
Introduction to Optical Remote Sensing Data
Module 2 · 33 Minutes to complete
Pre-Processing Optical Data
Module 3 · 42 Minutes to complete
The Many Uses of Optical Data
Module 4 · 40 Minutes to complete
Classification of Remote Sensing Satellite Data
Module 5 · 1 Hours to complete
Introduction to Active Remote Sensing Data: Synthetic Aperture Radar
Module 6 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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