Table of contents
Python hyperspectral remote sensing data processing and machine learning practice technology
Chapter 1 Hyperspectral Basics
Chapter 2 Hyperspectral Development Basics (Python)
Chapter 3 Hyperspectral Machine Learning Technology (python)
Chapter Four Typical Case Operation Practice
Matlab hyperspectral remote sensing data processing and mixed pixel decomposition
Chapter 2 Matlab Development Fundamentals
Chapter 3 Matlab hyperspectral data processing technology
Chapter 4 Matlab Hybrid Pixel Decomposition Technology
Chapter Five Typical Case Operation Practice
Python hyperspectral remote sensing data processing and machine learning practice technology
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.
You will understand the "underlying logic" of hyperspectral remote sensing through basic theories such as hyperspectral remote sensing, electromagnetic spectrum, the role of electromagnetic spectrum and matter, and spectral imaging mechanism; from hyperspectral data processing, spectral feature analysis, image classification, mixed pixels Master the "methodology" of hyperspectral remote sensing in technologies such as decomposition; in specific practical cases, learn to use the above principles and technical methods to improve the application ability of hyperspectral technology.
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
A brief 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
2. Hyperspectral sensor and data acquisition
Hyperspectral sensor types
How to acquire hyperspectral data
Challenges and limitations of hyperspectral data acquisition
Three hyperspectral data preprocessing
Physical meaning of hyperspectral images
Radiometric calibration
Atmospheric correction
Spectral smoothing and resampling
Four hyperspectral analysis
Spectral feature extraction
Dimension reduction techniques (such as PCA, MNF)
Hyperspectral classification, regression, target detection
Mixed pixel decomposition method
Five 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)
An introduction to Python programming
Introduction to Python
Variables and data types
Control structure
Functions and modules
File processing
Third-party packages and virtual environments
Two 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
Three 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)
One machine learning overview and python practice
Introduction to machine learning
Sciki learn introduction
Data and algorithm selection
General learning process
Machine learning model
Two 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
Three deep learning overview and python practice
Introduction to Deep Learning
PyTorch Overview
PyTorch Development Fundamentals
PyTorch Case Analysis
Four 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
A 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)
Two 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)
Three cases of soil quality assessment
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
Four cases of wood moisture content evaluation
Non-destructive testing principle
Non-destructive testing of wood
Wood moisture content testing practice
Matlab hyperspectral remote sensing data processing and mixed pixel decomposition
The tutorial 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. In the method section, hyperspectral technology is combined with MATLAB tools, and the rich toolbox of MATLAB is used to quickly reproduce the hyperspectral data processing and analysis process, and provide efficient feedback on the learned theories and methods. At the same time, give full play to the succinct and easy-to-operate programming language of MATLAB scratch paper, and analyze each line of code. In practice, through hyperspectral mineral identification, plant water content extraction, soil organic carbon assessment and other cases, provide reference for technical service solutions in the field of hyperspectral applications, combined with MATLAB matrix calculation, scientific data visualization, data processing and machine learning, image Processing and other functional modules, in-depth introduction to the development of application functions of hyperspectral technology.
You will understand the "underlying logic" of hyperspectral remote sensing through basic theories such as hyperspectral remote sensing, electromagnetic spectrum, the role of electromagnetic spectrum and matter, and spectral imaging mechanism; from hyperspectral data processing, spectral feature analysis, image classification, mixed pixels Master the "methodology" of hyperspectral remote sensing in technologies such as decomposition; in specific practical cases, learn to use the above principles and technical methods to improve the application ability of hyperspectral technology.
Chapter 1 Theoretical Basis
1. Hyperspectral remote sensing
What is hyperspectral remote sensing? —Basic concepts of hyperspectral remote sensing;
three characteristics of hyperspectral remote sensing—high spectral resolution, continuous spectral channels, and spectral imaging;
why is hyperspectral remote sensing useful? —The motivation and process of hyperspectral remote sensing, analysis of hyperspectral technology hotspots based on cite space.
2. Hyperspectral remote sensing imaging and data processing
Digital Rubik's Cube Game - hyperspectral imaging mechanism and imaging spectrometer;
what is actually being talked about when talking about reflectance data? — Hyperspectral remote sensing data types, parameters, metadata data preprocessing (radiation correction, atmospheric correction);
why is hyperspectral dimensionality reduced? — Spectral feature extraction, principal component analysis (PCA), minimum noise separation (MNF).
3. Hyperspectral remote sensing image classification and mixed pixel decomposition
Hyperspectral remote sensing image classification and recognition, supervised classification and unsupervised classification.
Mixed everywhere—mixed spectrum formation, physical mechanism;
linear and nonlinear models—mixed pixel decomposition model, linear spectrum mixed physics, mathematical model, Hapke nonlinear model.
Chapter 2 Matlab Development Fundamentals
1. Matlab software introduction and installation, common function introduction
Matlab version introduction, installation;
Matlab software interface, common function introduction;
those pits that have been stepped on in the past - common errors and usage attention, path problems, etc.
2. Matlab hyperspectral image processing framework
Organization and analysis of Matlab hyperspectral image processing framework;
Introduction to the use of APP-hyperspectral viewer. Main interface, band selection, band combination image display and spectrum visualization;
six groups of functions including data reading and writing visualization, enhancement, correction, dimensionality reduction, spectral unmixing, and spectral matching;
data preprocessing (radiation correction, atmospheric correction) Matlab module introduction and analysis.
3. Matlab selected cases and analysis
Introduction and analysis of hyperspectral remote sensing image classification cases, SAM image classification;
introduction and analysis of hyperspectral remote sensing image unmixing cases, HFC, N-FINDR, spectralMatch, SID and other programs.
Chapter 3 Matlab hyperspectral data processing technology
1. Hyperspectral imaging data processing and matlab realization of
GF-5, resource 02D satellite hyperspectral image data reading visualization (APP, function)
2D\3D hyperspectral data matrix transformation (function)
2. Ground spectrum measurement data processing and matlab implementation
Portable surface object spectrometer (asd), data reading, visualization (function)
reflectivity factor data calculation (function)
spectral curve display visualization (function)
3. Hyperspectral data regression quantitative analysis and matlab implementation
Hyperspectral regression analysis data sorting (function)
regression learner, random forest, linear, support vector machine, etc. (APP, function)
regression analysis results, error analysis visualization. (APP, function)
Chapter 4 Matlab Hybrid Pixel Decomposition Technology
1. Evaluation of the number of hyperspectral endmembers and matlab implementation of
Harsanyi-Farrand-Chang (NWHFC) noise whitening method and program implementation of Hysime hyperspectral data.
Code analysis of Hysime endmember quantity evaluation method.
2. Endmember spectrum extraction and matlab implementation
Use PPI, VCA and other methods to extract the endmember spectrum of hyperspectral data.
Code parsing for VCA endmember spectral extraction.
3. Endmember content evaluation and matlab implementation
The least squares and sparse calculation methods are used to evaluate the endmember content of hyperspectral data.
Code parsing for least squares endmember content evaluation method.
Chapter Five Typical Case Operation Practice
1. Mineral mapping case: Taking a certain area in Gansu as an example, using resource 02E data to extract and quantitatively evaluate sericite, chlorite and other altered mineral information. It involves reading hyperspectral images in the study area, evaluating the number of mineral species, extracting mineral endmember spectra, using spectral libraries for identification, evaluating mineral content, data processing, mineral map visualization, and result output, etc.
2. Wood moisture content algorithm case: using regression learner to quantitatively analyze the moisture content of forest wood sample data, involving hyperspectral data reading, writing, hyperspectral regression analysis data sorting, regression learner, random forest, linear, support Water content evaluation and error analysis visualization such as vector machine. Regression analysis result visualization, result output, etc.
3. Soil quality assessment case: Evaluate soil quality parameters based on aerial hyperspectral and ground spectral test data, involving aerial and ground hyperspectral soil survey program design, overall preprocessing of hyperspectral data, modeling of soil quality parameters, and visualization of results wait.
Python hyperspectral remote sensing data processing and machine learning practice technology_WangYan2022's blog-CSDN blog 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 comprehending by analogy, I will share it with you in a way that even remote sensing "novice" can easily accept. https://blog.csdn.net/WangYan2022/article/details/130314143?spm=1001.2014.3001.5502 Matlab hyperspectral remote sensing data processing and mixed pixel decomposition_matlab remote sensing data processing-CSDN blog You will pass hyperspectral remote sensing, electromagnetic waves Spectrum, electromagnetic spectrum and the role of matter, spectral imaging mechanism and other basic theories, understand the "underlying logic" of hyperspectral remote sensing; master hyperspectral remote sensing from technologies such as hyperspectral data processing, spectral feature analysis, image classification, and mixed pixel decomposition "Methodology"; in specific practical cases, learn to use the above principles and technical methods to improve the application ability of hyperspectral technology.
https://blog.csdn.net/WangYan2022/article/details/127636772?spm=1001.2014.3001.5502