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Filtering applies a kernel or window of coefficients over raster data to enhance features or reduce noise, often used in image processing.

Normalized Difference Vegetation Index (NDVI) calculation is used to measure vegetation health by comparing the difference between near-infrared and visible red light reflected by plants.

Pan-sharpening combines higher-resolution panchromatic data with lower-resolution multispectral data to produce an image with the best qualities of both.

Density slicing segments a continuous raster’s pixel values into discrete ranges to facilitate data visualization and interpretation.

Zonal statistics calculates summary statistics (e.g., mean, sum, max, min) for raster data within specified zones, often used in habitat and land use analysis.

Raster to vector conversion is the transformation of raster data, such as pixels, into vector format, such as points, lines, or polygons, facilitating analysis in different GIS applications.

Radiometric correction is performed to correct a raster image for sensor irregularities and atmospheric conditions, improving the data accuracy.

Mosaicking is the process of spatially combining multiple raster datasets to create a continuous image, useful for creating large-area maps.

Reclassification involves altering the pixel values of a raster dataset based on a set of criteria, typically to simplify or tailor the data for specific analysis.

Buffer generation creates zones of a specified distance around raster features, often used in proximity analysis and environmental impact studies.

Cost surface analysis assigns each raster cell a cost based on the effort or resources needed to traverse that cell, aiding in optimal path and site selection applications.

Contrast stretching enhances the dynamic range of raster image pixel values, often to improve visualization of features in imagery data.

Resampling is the process of interpolating new pixel values while transforming raster data, often due to changes in resolution or coordinate transformation.

Spatial interpolation predicts unknown values across a surface based on known data points, used in creating continuous raster surfaces from point data.