Methods used to process planetary remote sensing data, including image registration, filtering, and spatial analysis.
Remote Sensing: Remote sensing is the process of gathering information about the earth's surface using sensors mounted on satellites or aircraft.
Planetary Science: Planetary science is the study of planets in our solar system, including their formation, composition, and evolution.
Data Acquisition: Data acquisition is the process of collecting data using various instruments and sensors.
Image Processing: Image processing involves various techniques for enhancing and analyzing digital images obtained from remote sensing.
Radiometry: Radiometry is the study of electromagnetic radiation, including the measurement of its intensity and spectral properties.
Spectroscopy: Spectroscopy is the study of the interaction between matter and electromagnetic radiation, including the absorption, reflection, and emission of light.
GIS (Geographic Information Systems): GIS involves the use of computer technology to analyze and manipulate spatial data.
Remote Sensing Platforms: This includes different types of remote sensing platforms, such as satellites, aircraft, and drones.
Data fusion: Data fusion involves the integration of data from different sensors to produce more accurate and comprehensive information.
Machine Learning: Machine learning is a field of computer science that involves the development of algorithms that can learn patterns and make predictions from data.
Data Mining: Data mining is the process of discovering patterns and insights in large datasets.
Data Visualisation: Data visualisation involves the creation of visual representations of data to improve understanding and communication.
Statistical Analysis: Statistical analysis involves the use of statistical methods to analyze data and test hypotheses.
Image Classification: Image classification involves the categorization of images into different classes using algorithms.
Remote Sensing Applications: Various applications of remote sensing, such as land use mapping, forest monitoring, and weather forecasting.
Radiometric Calibration: This type of data processing involves converting raw sensor signals into physically meaningful quantities such as reflectance or temperature.
Geometric Correction: Geometric correction is used to correct distortions introduced during the imaging process. This includes correcting for distortion caused by Earth's atmosphere, topography, or spacecraft motion.
Image Enhancement: Image enhancement is used to improve the visual interpretation of images. This includes techniques such as contrast stretching, filtering, and edge detection, which can help to highlight specific features in the image.
Feature Extraction: Feature extraction is used to identify and isolate specific features in an image. This includes techniques such as classification, where objects are grouped together based on their spectral or structural characteristics.
Image Fusion: Image fusion is used to combine multiple images from different sensors or wavelengths into a single composite image. This can improve the quality of the image and increase the amount of information that can be extracted.
Data Fusion: Data fusion involves combining data from multiple sources, such as different imaging sensors or datasets, to provide a more comprehensive view of the target area.
Time Series Analysis: Time series analysis involves analyzing data over time to identify patterns or trends. This can be used to track changes in a specific feature or to monitor changes in the Earth's surface over time.
Machine Learning: Machine learning involves using algorithms to automatically identify patterns or features in an image. This can be used to help automate the data processing and analysis process.
Statistical Analysis: Statistical analysis is used to quantify the reliability and accuracy of the data. This includes techniques such as error analysis and confidence interval calculation.
Model Validation: Model validation involves comparing the results of a model or simulation to actual measurements to assess its accuracy and reliability. This can help to improve the predictive capabilities of the model.