Hyperspectral satellite image classification Using Deep CNNs
What you'll learn
- Concepts/basics of Convolutional Neural Networks
- Visualizing Hyperspectral data in Google Colab
- 1-Dimensional Convolutional Neural Network development
- 2-Dimensional Convolutional Neural Network development
- 3-Dimensional Convolutional Neural Network development
- Deep machine learning models training and validation in Google Colab
- Developing different advanced machine learning algorithms in Google Colab
- Land Use Land Cover (LULC) mapping with Hyperspectral satellite imagery
Requirements
- Basics of Remote Sensing
- Basics of Programming
Description
Land cover mapping is a critical aspect of Earth’s surface monitoring and mapping. In this course, Land Use Land Cover Mapping utilizing Hyperspectral satellite imagery is covered. You will learn how to develop 1-Dimensional, 2-Dimensional, 3-Dimensional, and Hybrid Convolutional Neural Networks (CNNs) using Google Colab. The discussed and developed methods can be utilized for different object/feature extraction and mapping (i.e., urban region extraction from high-resolution satellite imagery). Remote sensing is a powerful tool that can be used to identify and classify different land types, assess vegetation conditions, and estimate environmental changes. The use of Google Colab will significantly help you to decrease the issues encountered by software and platforms, such as Anaconda. There is a much lower need for library installation in the Google Colab, resulting in faster and more reliable classification map generation. The validation of the developed models is also covered. In summary, remote sensing and GIS technologies are widely used for land cover mapping. They provide accurate and timely information that is critical for monitoring and managing natural resources.
Highlights:
1. Learn the concepts of Convolutional Neural Networks (CNNs)
2. Learn how to develop CNN models
3. Learn how to classify Hyperspectral satellite imagery using python programming language
4. Learn how to validate a CNN model
5. Learn to read and import your data from your Google Drive into Google Colab
6. Map Land use land covers utilizing Hyperspectral satellite data with different variations of CNN models
7. Learn how to validate a machine-learning model
Who this course is for:
- Students and professionals interested in writing and publishing high-quality papers
- Remote sensing engineers
- GIS engineers
- Govt sector agriculture scientists
- Master students of GIS and Remote Sensing
- Data scientists interested in Remote Sensing image processing
- Ph.D. students of Data science, GIS, and Remote Sensing
Instructor
- 3.9 Instructor Rating
- 80 Reviews
- 320 Students
- 3 Courses
Ali Jamali is an experienced researcher with a demonstrated history of working in higher education, skilled in statistics, mathematics, GIS, remote sensing, image processing, machine learning, and algorithm optimization, with a Doctor of Philosophy (Ph.D.) focused in Geoinformatics from Universiti Teknologi Malaysia (UTM). His current research interest includes remote sensing image processing based on advanced mathematical and statistical algorithms. I have developed novel shallow and deep learning algorithms for satellite image processing (i.e., remote sensing) which are mainly focused on ecological, deforestation, and wetland mapping. I specialize in Python programming language with more than a decade of experience in different programming languages, such as MATLAB, R, and Python. Currently, I am working with internationally leading machine learning institutes to develop novel deep learning algorithms to be utilized in the Remote Sensing field.