How to evaluate the accuracy and precision of remote sensing data, including the use of ground truth data and statistical methods.
Basics of Remote Sensing: Understanding the terms and concepts of remote sensing, including electromagnetic radiation, spectral signatures, and sensor systems.
Image Interpretation: Developing the skills to interpret and analyze remote sensing imagery for various applications.
Digital Image Processing: Methods used to enhance, manipulate and analyze digital images for remote sensing applications.
Accuracy Assessment: The process of evaluating how well the data produced by remote sensing corresponds with actual conditions on the ground.
Error Analysis: In-depth examination of the level of accuracy of remote sensing data, including estimation of error budgets, error sources, and the propagation of errors.
Sampling Techniques: Methods used to obtain representative samples of the study area for accuracy assessment purposes.
Classification Techniques: Using data from remote sensing to classify features on the ground.
Spatial Analysis: Analyzing and interpreting data using geographic information systems (GIS) and spatial statistics.
Data Validation: Procedures used to validate datasets produced by remote sensing, including ground-truthing, reference data, and cross-validation.
Data Fusion: Combining data from different sensors or sources to improve the accuracy of the data.
Temporal Analysis: Using remote sensing data to monitor changes in the earth's surface over time.
Uncertainty Analysis: The assessment of uncertainty associated with remote sensing data and the impact of this uncertainty on the analysis conducted.
Quality Control: Ensuring that remote sensing data are consistent, accurate, and reliable through the use of procedures and protocols.
Applications of Remote Sensing: Understanding the various applications of remote sensing in earth science, including land use/land cover mapping, environmental monitoring, and mineralogical exploration.
Future Trends: Analyzing current and future trends in remote sensing technology and applications.
Error Matrices: Error matrices are used in accuracy assessment and error analysis in Earth Sciences to quantify the discrepancies between reference and classified data by categorizing measurements into different classes and comparing observed and expected values.
Confusion Matrices: Confusion matrices are a tabular representation used to evaluate the accuracy of classification models by comparing predicted and observed values.
Kappa Coefficient: The Kappa coefficient is a statistical measure used to assess and quantify the agreement between two sets of categorical data by accounting for chance agreement.
Overall Accuracy Assessment: Overall Accuracy Assessment in Earth Sciences is the evaluation of the overall correctness of the results obtained from a data analysis or modeling process.
User's accuracy and Producer's accuracy: In Earth Sciences and Accuracy Assessment and Error Analysis, User's accuracy refers to the probability of correctly identifying a specific category or feature using remote sensing data, while Producer's accuracy refers to the probability of a category or feature being correctly classified by remote sensing data.
Area Estimation Error: Area estimation error refers to the discrepancy between the actual area of a geographical feature and the estimated area obtained through remote sensing or other measurement techniques.
Root Mean Square Error: Root Mean Square Error (RMSE) is a statistical measure used to quantify the average difference between observed and predicted values in Earth Sciences by taking the square root of the sum of squared differences divided by the number of observations.
Mean Absolute Error: Mean Absolute Error (MAE) is a measure used in Earth Sciences to quantify the average magnitude of errors between predicted and observed values, providing a measure of overall accuracy.
Empirical Distribution Function: The empirical distribution function is a statistical tool used in Earth Sciences to estimate the probability distribution of a population based on a sample, evaluating the accuracy of the observed data against theoretical predictions.
Receiver Operating Characteristic Curve Analysis: Receiver Operating Characteristic (ROC) Curve Analysis is a graphical tool used in Earth Sciences to evaluate and determine the performance of classification models by plotting the true positive rate against the false-positive rate for different classification thresholds.
McNemar’s Test: McNemar's Test is a statistical test used to compare the differences in accuracy between two paired datasets.
Reference Data Analysis: Reference Data Analysis in Earth Sciences and Accuracy Assessment and Error Analysis involves evaluating the accuracy and quality of collected data by comparing it with reliable and benchmark reference datasets.
Visual Inspection of Classification Results: Visual inspection of classification results involves visually examining the accuracy of a classification map or image through a comparison with ground truth data or highly accurate reference data.
Cross-validation: Cross-validation is a statistical technique used to assess the accuracy and robustness of models or predictions by testing them on independent subsets of data.