Both active and passive remote sensing are commonly used methods for acquiring remotely sensed data. Active remote sensing involves sending out a signal and measuring the reflected or scattered signal, while passive remote sensing involves measuring the natural radiation emitted or reflected by an object.

Data preprocessing is the process of converting raw remotely sensed data into a usable format. It involves a series of steps such as radiometric correction, geometric correction, and atmospheric correction.

Histogram equalization, linear contrast stretching, and normalization are all common radiometric correction techniques used to remove the effects of sensor gain and offset.

Geometric correction is the process of correcting geometric distortions in remotely sensed data. It involves correcting for errors such as image skew, rotation, and scale.

Affine transformation, polynomial transformation, and rubber sheeting are all common geometric correction techniques used to correct for image skew.

Atmospheric correction is the process of removing the effects of the atmosphere from remotely sensed data. It involves correcting for errors such as atmospheric scattering, absorption, and path radiance.

Dark object subtraction, radiative transfer model, and empirical correction are all common atmospheric correction techniques used to correct for atmospheric scattering.

Image enhancement is the process of enhancing the visual appearance of remotely sensed data. It involves a series of techniques such as contrast stretching, color balancing, and filtering.

Histogram equalization, linear contrast stretching, and normalization are all common image enhancement techniques used to improve the contrast of an image.

Data analysis is the process of extracting meaningful information from remotely sensed data. It involves a series of techniques such as classification, regression, and change detection.

Maximum likelihood classification, support vector machine, and decision tree are all common data analysis techniques used to classify remotely sensed data.

Data interpretation is the process of interpreting the results of data analysis. It involves drawing conclusions and making decisions based on the analyzed data.

Visual interpretation, statistical analysis, and geospatial analysis are all common data interpretation techniques used to identify patterns and trends in remotely sensed data.

Data visualization is the process of presenting the results of data interpretation in a clear and concise manner. It involves creating maps, charts, graphs, and other visual representations of the data.

Choropleth map, dot map, and line map are all common data visualization techniques used to display the spatial distribution of data.