Table of contents
Chapter 1 Hyperspectral Basics
Chapter 2 Hyperspectral Development Basics (Python)
Chapter 3 Hyperspectral Machine Learning Technology (python)
Chapter Four Typical Case Operation Practice
This is the second season of hyperspectral remote sensing content (Season 1: Matlab hyperspectral remote sensing, data processing and mixed pixel decomposition practical technology application)
Summarizes the basic principles and core concepts in the field of hyperspectral remote sensing technology, uses programming language to reproduce classic data processing and application methods, and tracks the latest technological breakthroughs. After digesting and understanding, it is easy to use remote sensing "novice" The accepted method is shared with you.
The first season of the hyperspectral remote sensing course: Provide a set of hyperspectral data processing methods and application cases based on Matlab programming tools, in-depth explanation of hyperspectral remote sensing from three aspects: foundation, method and practice, through 8 core concepts, 4 Functional modules, 3 typical application scenarios, help everyone understand the "underlying logic" of hyperspectral remote sensing, master the "methodology" of hyperspectral remote sensing, and learn to use the above principles and technical methods in specific practical cases to improve students' hyperspectral The level of application capability of the technology.
The second season will continue to explain the technical concepts, processing methods and typical applications of hyperspectral remote sensing in depth, and on this basis, use python language to reproduce all the classic cases in the course, and prepare to deliver machine learning in the form of small topics Systematic courses to help you build your own structured knowledge system and method guide for hyperspectral remote sensing machine learning.
The second season still explains hyperspectral remote sensing from three aspects: foundation, method and practice. In the basic chapter, understand "hyperspectral" from the perspective of students, explain the basic concepts and theories of hyperspectral in a language that everyone can understand, and help students deeply understand the underlying scientific mechanism of this technology. Method article, combining hyperspectral technology with Python programming tools, focusing on the difficulties of high-frequency technology, clarifying the key points of development, quickly reproducing the process of hyperspectral data processing and analysis, and analyzing each line of code, and analyzing the learned theories and methods Give effective feedback. In practice, through cases such as hyperspectral mineral identification, wood water content extraction, soil organic carbon assessment, etc., provide reference for technical service solutions in the field of hyperspectral applications, combined with open source development libraries such as Python scientific computing, visualization, data processing and machine learning , an in-depth introduction to the application function development of hyperspectral technology.
Hyperspectral remote sensing information is of great value for us to understand the world. Most materials look the same to the human eye, but under the observation of hyperspectral remote sensing, they show unique "spectral characteristics". This ability to see "true color" has revolutionary potential for a series of applications such as precision agriculture, earth observation, art analysis, and medicine. I hope that through an easy-to-understand method, you can learn more about hyperspectral knowledge and technology. I wish you a happy study and gain something!
It is suitable for everyone who is interested in hyperspectral technology and wants to practice it with python.
Study, you will get:
- A full set of hyperspectral data processing methods and application cases (including python source code)
- A systematic solution combining hyperspectral and machine learning
- The latest technological breakthroughs explained and reproduced code
- Thematic sharing of scientific research project practice and learning methods
Chapter 1 Hyperspectral Basics
Lesson 1 Introduction to Hyperspectral Remote Sensing
- What is hyperspectral remote sensing?
- Why is hyperspectral remote sensing important?
- What is the difference between hyperspectral remote sensing and other remote sensing technologies?
- History and Development of Hyperspectral Remote Sensing
Lesson 2 Hyperspectral Sensors and Data Acquisition
- Hyperspectral Sensor Type
- How to obtain hyperspectral data
- Challenges and Limitations of Hyperspectral Data Acquisition
Lesson 3 Hyperspectral Data Preprocessing
- Physical significance of hyperspectral images
- radiometric calibration
- atmospheric correction
- Spectral smoothing and resampling
Lesson 4 Hyperspectral Analysis
- Spectral Feature Extraction
- Dimensionality reduction techniques (eg PCA, MNF)
- Hyperspectral classification, regression, object detection
- Hybrid Cell Decomposition Method
Lesson 5 Hyperspectral Applications
- Environmental monitoring (vegetation classification, water quality assessment)
- Agriculture (crop yield estimation, disease detection)
- Mineral exploration (mineral identification, geological survey)
- Urban planning (e.g. land use/cover classification, urban heat island analysis)
Chapter 2 Hyperspectral Development Basics (Python)
Lesson 1 Introduction to Python Programming
- Introduction to Python
- Variables and Data Types
- control structure
- Functions and Modules
- file processing
- Third-party packages and virtual environments
Lesson 2 Python Spatial Data Processing
- Introduction to Spatial Data and Python
- Introduction to Python Spatial Data Processing Library
- Python reads and writes spatial data files
- Python for Geospatial Analysis
Lesson 3 python hyperspectral data processing
- Python implementation of hyperspectral data reading
- Hyperspectral data preprocessing python implementation
- Hyperspectral mixed pixel decomposition python implementation
- Hyperspectral data visualization python implementation
Chapter 3 Hyperspectral Machine Learning Technology (python)
The first lesson machine learning overview and python practice
- Introduction to Machine Learning
- Introduction to sciki-learn
- Data and Algorithm Selection
- General Learning Process
- machine learning model
Lesson 2 Hyperspectral Machine Learning
- Application introduction of machine learning technology in hyperspectral data processing and analysis
- Hyperspectral data machine learning practice
- Machine Learning Model Performance Evaluation and Validation Techniques
Lesson 3 Overview of Deep Learning and Python Practice
- Introduction to Deep Learning
- PyTorch overview
- Basics of PyTorch Development
- PyTorch Case Study
Lesson 4 Hyperspectral Deep Learning
- Application of Autoencoders in Hyperspectral Data Analysis
- Applications of Convolutional Neural Networks (CNN) in Hyperspectral Data Analysis
- Application of Recurrent Neural Network (RNN) in Hyperspectral Data Analysis
- Hyperspectral Deep Learning Case Study
Chapter Four Typical Case Operation Practice
Lesson 1 Mineral Mapping Case
- Spectral Mechanism of Rock and Mineral
- Introduction of Hyperspectral Mineral Mapping Method
- Hyperspectral Data Mineral Mapping (ENVI)
- Hyperspectral Data Mineral Mapping (Python)
- Hyperspectral Data Mineral Mapping Machine Learning Case (Python)
Lesson 2 Agricultural Application Cases
- Vegetation Hyperspectral Mechanism
- Hyperspectral Data Crop Classification (ENVI)
- Hyperspectral Data Crop Identification and Classification (Python)
- Application of machine learning on hyperspectral data in agriculture (Python)
Lesson 3 Soil Quality Assessment Case
- Mechanism and Characteristics of Soil Spectroscopy
- Contents of Soil Quality Survey
- Ground Spectral Measurement and Sampling
- UAV hyperspectral survey and soil survey
- Analysis of hyperspectral soil machine learning program
Lesson 4 Wood Moisture Content Evaluation Case
- Principle of non-destructive testing
- Non-destructive testing of wood
- Wood moisture content test practice
Summary and Q&A
Review and Summary
Exchange Q&A
State-of-the-art technology presentations and discussions