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
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.
Matlab Hyperspectral Remote Sensing Data Processing and Mixed Pixel Decomposition Logic"; master the "methodology" of hyperspectral remote sensing from hyperspectral data processing, spectral feature analysis, image classification, mixed pixel decomposition and other technologies; in specific practical cases, learn to use the above principles and technical methods to improve hyperspectral technology application ability level. https://blog.csdn.net/WangYan2022/article/details/127636772?spm=1001.2014.3001.5502 The second quarter will continue to explain in depth the technical concepts, processing methods and typical applications of hyperspectral remote sensing, and on this basis, adopt The python language reproduces all the classic cases in the course, and prepares to deliver a systematic course of machine learning in the form of small topics to help you establish your own structured knowledge system and method guide for hyperspectral remote sensing machine learning.
The course of 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 easy-to-understand courses, you can learn more about hyperspectral knowledge and technology. I wish you a happy study and gain something!
This tutorial is suitable for everyone who is interested in hyperspectral technology and wants to practice it with python.
You will get:
1. A full set of hyperspectral data processing methods and application cases (including python source code)
2. A systematic solution combining hyperspectral and machine learning
3. The latest technological breakthrough explanation and reproduction code
4. Research project practice Thematic sharing of 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 types
How to acquire hyperspectral data
Challenges and limitations of hyperspectral data acquisition
Lesson 3 Hyperspectral Data Preprocessing
Physical meaning of hyperspectral images
Radiometric calibration
Atmospheric correction
Spectral smoothing and resampling
Lesson 4 Hyperspectral Analysis
Spectral feature extraction
Dimension reduction techniques (such as PCA, MNF)
Hyperspectral classification, regression, target detection
Mixed pixel 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 (such as 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 to read and write spatial data files
Python for geospatial analysis
Lesson 3 python hyperspectral data processing
Python realization of hyperspectral data reading Python realization of
hyperspectral data preprocessing Python realization of
hyperspectral mixed pixel decomposition Python realization of
hyperspectral data visualization
Chapter 3 Hyperspectral Machine Learning Technology (python)
The first lesson machine learning overview and python practice
Introduction to machine learning
Sciki learn introduction
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
Machine learning practice of hyperspectral data
Machine learning model performance evaluation and verification technology
Lesson 3 Overview of Deep Learning and Python Practice
Introduction to Deep Learning
PyTorch Overview
PyTorch Development Fundamentals
PyTorch Case Analysis
Lesson 4 Hyperspectral Deep Learning
Application of Autoencoder in Hyperspectral Data Analysis Application of
Convolutional Neural Network (CNN) in Hyperspectral Data Analysis Application of
Recurrent Neural Network (RNN) in Hyperspectral Data Analysis
A Case Study of Hyperspectral Deep Learning
Chapter Four Typical Case Operation Practice
Lesson 1 Mineral Mapping Case
Rock and mineral spectral mechanism
Introduction to 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
Hyperspectral Mechanism of Vegetation
Hyperspectral Data Crop Classification (ENVI)
Hyperspectral Data Crop Identification and Classification (Python)
Hyperspectral Data Agricultural Application Machine Learning Case (Python)
Lesson 3 Soil Quality Assessment Case
Soil Spectral Mechanism and Characteristics
Soil Quality Survey Contents
Ground Spectral Measurement and Sampling
UAV Hyperspectral Measurement and Soil Survey
Analysis of Hyperspectral Soil Machine Learning Program
Lesson 4 Wood Moisture Content Evaluation Case
Non-destructive testing principle
Non-destructive testing of wood
Wood moisture content testing practice
