In today's new round of Internet information technology development, emerging technologies such as drones, big data, artificial intelligence, and the Internet of Things are on the eve of a major explosion in all walks of life. In order to introduce artificial intelligence methods into the field of agricultural production. First of all, we will gradually get rid of manpower dependence in production operations such as planting and maintenance; we will build a smart energy-saving system in fertilization and irrigation; we will improve efficiency in output forecasting and commodity pricing production management. For these goals that smart agriculture urgently needs to achieve, the first problem to be solved is the acquisition and rapid analysis of multi-source data.
As a means of spatial big data, remote sensing technology can obtain a large amount of agricultural data from multi-time, multi-dimensional, multi-place perspectives. Data has significant advantages such as surface, real-time, non-contact, and non-injury detection, and is one of the important technologies that must be adopted in smart agriculture. It is mainly aimed at beginners (undergraduate students, junior graduate students) who are interested in UAV remote sensing with backgrounds in agriculture, forestry, ecology, and remote sensing, as well as beginners in MATLAB programming. By studying this course, students will master the whole chain theory and practical process of UAV remote sensing data preprocessing, the estimation theory and practical method of typical agricultural and forestry vegetation traits, programming practice using MATLAB (script and GUI development) and journal paper illustrations production etc. The courses learned can be used to support the development of scientific research or application projects, the advancement of research technology solutions, and the writing of journal papers, etc.
Chapter 1: Understanding Active and Passive UAV Remote Sensing Data
1. First understanding of active and passive UAV remote sensing data
1.1. UAV platform and coordinate system
1.2. Remote sensing load types and data
1.3. Flight parameter setting and calculation
1.4. Characteristics of UAV VS satellite active and passive remote sensing data
2. Read and write UAV remote sensing data
2.1. Reading and writing drone images with/without geographic coordinates
2.2. Reading and writing super-sized drone images
2.3. Read and write image metadata information
2.4. Reading and writing lidar/photogrammetry point clouds
Chapter 2: Preprocessing UAV Remote Sensing Data
1. Overview of remote sensing data preprocessing
1.1 Radiation signal reflected by ground objects
1.2 Characterization of two-way reflection characteristics of ground objects
1.3 Geometry Problems of UAV Images
2. Radiometric correction of drone images
2.1. Radiation correction of the optical measurement system
2.2. Albedo correction
2.3. BRDF and Shading Correction
3. Geometric correction of drone images
3.1. Geometric distortion correction of original image
3.2. Geometric registration of multispectral images
3.3. Orthophoto Geometry Correction
Chapter 3: Quantitative estimation of key traits of agricultural and forestry vegetation
1. Estimation of vegetation coverage fCover and photosynthetically active radiation absorption ratio fPAR
1.1. Estimation based on RGB image segmentation
1.2. Estimation based on pixel decomposition
1.3. Estimation based on point cloud
1.4. Estimation based on lidar echo
2. Estimation of leaf area index LAI
2.1. Estimation based on the void rate model
2.2. Estimation based on radiative transfer model
2.3. Estimation based on machine learning model
3. Estimation of chlorophyll content LCC
3.1 Understanding the blade radiative transfer model
3.2 Estimation based on radiative transfer model
3.3 Estimation based on vegetation index
Chapter 4: Fine Production of Journal Article Illustrations and Appdesigner Application Development
1. Produce beautiful illustrations for journal papers
1.1. The size, color and font of the paper illustrations
1.2. Production of scatter plots, histograms, line charts, violin plots, density maps, false color maps, etc.
2. Use Appdesigner for GUI development
2.1. Know Appdesigner
2.2. Function call and update
2.3. Parameter transfer between windows
