Appendix III: Ten (10) Specialty Areas - Remote Sensing/Imagry Science Curriculum Mapping to Knowledge Units-RS/Imagry Science Specialty Area

Transcription

1 III. Remote Sensing/Imagery Science Specialty Area 1. Knowledge Unit title: Remote Sensing Collection Platforms A. Knowledge Unit description and objective: Understand and be familiar with remote sensing collection platforms and apply this knowledge to solving spatio-temporal problems B. Requirement satisfaction: This KU is satisfied when seven (7) Topics and all Learning Objectives Remote Sensing Collection Platforms III.1C1 III.1C2 III.1C3 III.1C4 III.1C5 III.1C6 III.1C7 III.1C8 III.1C9 III.1C10 III.1C11 III.1C12 III.1D1 III.1D2 III.1D3 III.1D4 Appendix III: Ten (10) Specialty Areas - Remote Sensing/Imagry Science Curriculum Mapping to Knowledge Units-RS/Imagry Science Specialty Area Fundamentals of remote sensing platforms High and low altitude airborne remote sensing platforms Platform/sensor position/orientation measurement Effects of airborne sensor flight path, speed and control parameters on data collection Airborne remote sensing data applications Space-borne remote sensing platforms and sensors Imaging satellite orbits (e.g. geosynchronous, sun synchronous, etc.) Space-borne remote sensing data applications Un-manned aerial vehicles (UAV), remotely piloted aircraft (RPA), unmanned aircraft systems (UAS) remote sensing platforms UAV/RPA/UAS mission planning UAV/RPA/UAS data collection and processing UAV/RPA/UAS remote sensing applications Define the basic theories, processes of remote sensing platforms and sensors Define the common applications for data from remote sensing platforms/sensors Describe the effects and parameters on imagery in relation to airborne sensor flight path, speed and control (pitch, yaw, roll) Discuss airborne remote sensing platform applications

2 III.1D5 III.1D6 III.1D7 Remote Sensing Collection Platforms Describe the current constellation of space-borne remote sensing platforms in terms of their technologies, orbital parameter capabilities and their applications Describe the difference between nadir-looking and "agile" satellites (i.e., Worldview-2) Describe UAV/RPS/UAS remote sensing data collection and their applications 2. Knowledge Unit title: Radiometry A. Knowledge Unit description and objective: Skills and knowledge required to comprehend the quantitative measurement of electromagnetic energy and its application to simple imaging systems Radiometry III.2C1 III.2C2 III.2C3 III.2C4 III.2C5 III.2D1 III.2D2 III.2D3 III.2D4 Radiometric and photometric quantities (SI units) Quantitative measurement of electromagnetic energy and its application to imaging sensors Optical properties of materials and components Ways to characterize radiometric performance of detectors Radiometric calibration/normalization principles, approaches and tools Define quantitative measurement of electromagnetic energy and its application to imaging sensors Define Radiometric and photometric quantities (SI units) Describe optical properties of materials and components Explain principles, approaches and tools of radiometric calibration/normalization

3 3. Knowledge Unit title: Electro-optical (EO) Sensor Science A. Knowledge Unit description and objective: Understand and be familiar with passive visible and infrared phenomenology, theory, EO sensor design and their applications. Electro-optical (EO) Sensor Science III.3C1 III.3C2 III.3C3 III.3C4 III.3C5 III.3C6 Types of electro-optical sensors Ultraviolet, visible and shortwave-midwave-longwave infrared spectral measurement theories, principles, techniques and their applications Reflected and emitted energy spectral signatures EO data corrections (atmospheric interactions, windows and absorption regions/bands) Theories, principles, types and designs of EO sensors EO sensor applications Describe types of EO sensors (passive/active collector, staring/scanning array, single/multiple waveband) III.3D1 III.3D2 III.3D3 III.3D4 Explain atmospheric effects and corrections for EO sensors Describe reflected and emitted energy and spectral signature generation. Describe EO sensor applications based on sensor type

4 4. Knowledge Unit title: Thermal Remote Sensing A. Knowledge Unit description: Skills and basic knowledge required to comprehend concepts, issues and applications relating to thermal imaging systems B. Requirement satisfaction: This KU is satisfied when at least seven (7) Topics and all Learning Objectives Thermal Remote Sensing III.4C1 III.4C2 III.4C3 III.4C4 III.4C5 III.4C6 III.4C7 III.4C8 III.4C9 III.4C10 III.4C11 III.4D1 III.4D2 Principles of thermal remote sensing (Planck s Function, Stefan-Boltzman Law Atmospheric effects on thermal remote sensing Spectral emissivity and kinetic temperature Factors affecting kinetic temperature Radiant temperature Thermal remote sensing data acquisition modes (active/passive, single/multiple waveband, day/night collection) and thermal sensor platforms Spatial resolution and geometric corrections Thermal remote sensing applications Measured radiance as a function of observed material temperature and emissivity Methods to separate temperature and emissivity Thermal hyperspectral systems Describe the principles of thermal remote sensed imaging, platforms, sensor types, its limitations and data processing methodologies Describe thermal remote sensed data applications 5. Knowledge Unit title: Radar Remote Sensing A. Knowledge Unit description and objective: Understand and comprehend radar remote sensing, theory, design and applications. Basic Radar Science III.5C1 III.5C2 III.5C3 Microwave remote sensor system types Microwave frequency measurement theory, techniques and design Applications of radar remote sensing

5 III.5D1 III.5D2 III.5D3 III.5D4 Basic Radar Science Define the different types of microwave remote sensor systems Describe microwave signal generation, propagation, target interaction, receipt, recording and measurement Discuss atmospheric effects on radar operation Describe applications of microwave remote sensing 6. Knowledge Unit title: Lidar Remote Sensing A. Knowledge Unit description and objective: Comprehend skills and knowledge of Lidar remote sensing, how Lidar data is collected, processed and its applications. B. Requirement satisfaction: This KU is satisfied when at least all Topics and all Learning Objectives Lidar Remote Sensing III.6C1 III.6C2 III.6C3 III.6C4 III.6C5 Fundamentals of Lidar remote sensing Lidar sensors Lidar data formats Lidar analysis tools and analytical methodologies Lidar point classification Lidar remote sensing applications III.6C6 III.6D1 III.6D2 III.6D3 III.6D4 Describe the fundamentals of Lidar remote sensing List and define Lidar data types Describe Lidar analysis tools and analytical methodologies Explain Lidar point classification

6 7. Knowledge Unit title: Remote Sensing Data Analysis A. Knowledge Unit description and objective: Introduction to the basic applications of quantitative remote sensing data analysis and the mathematical tools used for data exploitation Remote Sensing Data Analysis III.7C1 III.7C2 III.7C3 III.7C4 III.7C5 III.7D1 III.7D2 III.7D3 Mathematical frameworks for algorithm development (multivariate statistics, linear algebra and subspace geometry, spectral linear mixture model, basic signal detection theory) Spectral Classification Algorithms (supervised and unsupervised, minimum distance to the mean, Mahalanobis distance, Gaussian maximum likelihood) Spectral signature analysis algorithms (band ratio analysis such as NDVI, NDWI), geologic mineral analysis Spectral Detection algorithms (anomaly detection such as RX, change detection such as chronocrome, covariance equalization), target detection (such as GLRT, spectral matched filter, ACE, CEM) Linear spectral un-mixing Explain the (semi-) automated applications of quantitative remote sensing image analysis Describe the mathematical principles behind quantitative remote sensing image analysis Identify the limitations of quantitative remote sensing image analysis

7 8. Knowledge Unit title: Digital Image Processing A. Knowledge Unit description and objective: Be familiar with and understand digital processing of remote sensing imagery and data. B. Requirement satisfaction: This KU is satisfied when at least five (5) Topics and at least four (4) Learning Objectives Digital Image Processing III.8C1 III.8C2 III.8C3 III.8C4 III.8C5 III.8C6 III.8C7 III.8D1 Radiometric and geometric correction Image enhancement, transformation, filtering, resampling, mosaicking, interpolation and restoration. Image classification Raster/data conversion, compression, storage format and representation Image processing algorithms and techniques to support image enhancement; image filtering, resampling, interpolation Automatic and assisted feature recognition algorithms and their limitations Point and feature matching algorithms Define processes to prepare raster imagery for analysis III.8D2 III.8D3 III.8D4 III.8D5 Define or demonstrate supervised and unsupervised classification Apply methods to classify an image into various features and classes Explain the concepts of digital counts, image histogram processing, and compression Demonstrate basic proficiency in the computational manipulation of imagery

8 9. Knowledge Unit title: Computational Radiometry A. Knowledge Unit description and objective: Understand skills and knowledge required to develop, generate, and apply synthetic scenes Computational Radiometry III.9C1 III.9C2 III.9C3 III.9C4 III.9C5 III.9D1 III.9D2 III.9D3 Imaging system modeling (scene/sensor/processing parameters) Understanding of material and optical properties Understanding of atmospheric modeling Scene construction basics and geometry modeling Applications of computational radiometry Discuss imaging system modeling Perform, test and evaluate imaging modeling Discus the applications of computational radiometry 10. Knowledge Unit title: Imagery/Satellite Image Time Series Analysis A. Knowledge Unit description: Skills and basic knowledge of temporal imagery/satellite image time series analysis and its applications. B. Requirement satisfaction: This KU is satisfied when all Topics and Learning Objectives are met. Imagery Time Series Analysis III.10C1 III.10C2 Imagery (ITS)/Satellite image time series (SITS) analysis Time series analytical tools, methodologies and processes III.10C3 III.10C4 ITS/SITS time scales Image time series applications

9 III.10D1 III.10D2 III.10D3 Imagery Time Series Analysis Explain ITS/SITS analytical tools, methodologies and processes Discuss the merits and differences between the various ITS/SITS time scales Discuss the various applications for ITS/SITS analysis