Remote Sensing

Compares multispectral images from two dates and generates per-pixel change vectors to locate and characterize changes.

Detects changes from the difference between images.

Detects image changes using principal components to localize modifications between temporal observations.

Compares classes between dates to identify changes between temporal observations.

Inserts a memory function into multitemporal analysis to detect persistent changes between images.

Examines spectral signature overlap between training regions or samples of different classes before image classification.

Classifies pixels using K nearest neighbors with fuzzy membership.

Simplifies classified regions by removing small patches and isolated edges.

Generalizes a raster using similarity between classes.

Groups pixels into K classes via iterative means.

Classifies pixels using the K nearest neighbors.

Estimates continuous values using the K nearest neighbors.

Fits a logistic model from image variables to estimate probability of class membership.

Classifies pixels by minimum distance to class signatures.

Clusters pixels using a modified variant of K-means.

Classifies pixels by normalized distance to training signatures.

Separates classes using automatic Otsu thresholding.

Classifies pixels using per-class value ranges.

Classifies pixels with a random forest trained from known samples.

Predicts continuous per-pixel values using a regression random forest.

Classifies pixels using support vector machines.

Estimates continuous values using support vector regression.

Detects image edges with the Canny method and outputs an outline raster.

Identifies corner patterns in boolean images via template matching and outputs a corner raster.

Applies a Laplacian filter to an image and generates a filtered raster.

Applies a Laplacian of Gaussians filter to an image and generates a filtered raster.

Applies a Prewitt filter to an image and generates a filtered raster.

Applies a Roberts cross filter to an image and generates a filtered raster.

Applies a Sobel filter to an image and generates a filtered raster.

Adjusts contrast of a multispectral composite by balancing tonal distributions across bands.

Creates a color composite to support enhancement and contrast tasks.

Reduces correlation between bands and stretches contrast to highlight spectral differences.

Combines selected bands into a false color composite to highlight land covers or phenomena.

Adjusts brightness values with a gamma curve to lighten or darken tonal ranges.

Redistributes values along a Gaussian curve to improve visual image contrast.

Redistributes values according to the histogram to increase image contrast.

Adjusts the tonal distribution of an image to match a target histogram.

Computes a histogram matching between two images to support enhancement and contrast tasks.

Converts intensity, hue, and saturation components into red, green, and blue bands.

Stretches values between defined minimum and maximum to use the full display range.

Combines multiple contiguous images into continuous coverage preserving pixel values.

Joins overlapping images with gradual edge transitions to reduce visible seams.

Computes the normalized difference between two bands to highlight a specific spectral property.

Fuses panchromatic detail with multispectral bands for higher spatial sharpness.

Clips extreme histogram percentages and stretches the central range of values.

Applies a piecewise transformation to enhance selected tonal ranges.

Changes image resolution or alignment using the selected resampling method.

Converts red, green, and blue bands into intensity, hue, and saturation components.

Applies a sigmoidal curve to increase contrast in the mid-range image values.

Separates a color composite into its individual bands for separate analysis.

Stretches values around the mean using standard deviations to improve contrast.

Combines visible red, green, and blue bands to approximate natural-color display.

Applies an adaptive filter to an image and outputs a filtered raster.

Applies an anisotropic diffusion filter to an image and outputs a filtered raster.

Applies a bilateral filter to an image and outputs a filtered raster.

Applies dilation followed by erosion to close small gaps in a binary image.

Applies a conservative smoothing filter to an image and outputs a filtered raster.

Applies the difference-of-Gaussians filter method to an image.

Applies a diversity filter to an image and outputs a filtered raster.

Applies an edge-preserving mean filter to an image and outputs a filtered raster.

Applies an emboss filter to an image and outputs a filtered raster.

Applies a fast near-Gaussian filter to an image and outputs a filtered raster.

Flips a raster about its horizontal or vertical axis, producing an inverted-orientation image.

Applies a Frangi filter to an image and outputs a filtered raster.

Applies a Gabor filter bank method to an image.

Applies a Gaussian filter to an image and outputs a filtered raster.

Computes texture measures via co-occurrence matrices to describe local image variation.

Applies a guided filter to an image and outputs a filtered raster.

Applies a high-pass bilateral filter to an image and outputs a filtered raster.

Applies a high-pass filter to an image and outputs a filtered raster.

Applies a high-pass median filter to an image and outputs a filtered raster.

Computes an integral image that accelerates rectangular sums over the original image.

Smooths each pixel with the mean of its most similar neighbors to preserve edges.

Applies a Kuwahara filter to an image and outputs a filtered raster.

Applies a Lee filter to an image and outputs a filtered raster.

Classifies ground LiDAR points by removing vegetation and building returns.

Applies a line detection filter to an image and outputs a filtered raster.

Reduces raster lines to a one-pixel-wide central axis while preserving connectivity.

Applies a majority filter to an image and outputs a filtered raster.

Applies a maximum filter to an image and outputs a filtered raster.

Applies a mean filter to an image and outputs a filtered raster.

Applies a median filter to an image and outputs a filtered raster.

Applies a minimum filter to an image and outputs a filtered raster.

Applies a non-local means filter to an image and outputs a filtered raster.

Applies an Olympic filter to an image and outputs a filtered raster.

Applies erosion followed by dilation to remove small objects from a binary image.

Applies a percentile filter to an image and outputs a filtered raster.

Applies a range filter to an image and outputs a filtered raster.

Removes short branches from raster lines to clean up skeletons and extracted networks.

Applies a Savitzky-Golay 2D filter to an image and outputs a filtered raster.

Applies a Scharr filter to an image and outputs a filtered raster.

Applies a standard deviation filter to an image and outputs a filtered raster.

Thickens linear cells in a raster to reinforce narrow or broken connections.

Highlights small objects via the difference between the image and its morphological opening.

Applies a total (sum) filter to an image and outputs a filtered raster.

Sharpens by combining the image with a blurred version to emphasize edges.

Applies a user-defined weights filter to an image and outputs a filtered raster.

Applies an adaptive Wiener filter to reduce noise while preserving relevant image structures.

Links coarse and fine segments to build a multiscale object hierarchy.

Classifies segments by combining multiple trained models and assigns each object a class.

Classifies segments with a random forest trained from attributes and samples.

Assigns a class to each segment based on explicit rules over its attributes.

Classifies segments by applying rules at user-defined hierarchical levels.

Classifies segments using a support vector machine trained with samples.

Compares predicted and reference classes to produce per-segment accuracy measures.

Compares segments against reference data to measure over- and under-segmentation.

Splits images into homogeneous regions using threshold, step size, and minimum area parameters.

Generates an HTML page with a slider to visually compare two images.

Generates profiles of values across multiple images at selected points.

Collects artifacts from an OBIA workflow into a reproducible audit report.

Runs a list of OBIA jobs in batch and logs their outputs.

Runs a basic segmentation and classification workflow from images and training data.

Converts per-segment class probabilities into spatial probability layers.

Computes context attributes from each object's neighboring segments.

Computes segment shape characteristics such as size and compactness for object-based analysis.

Computes spectral statistics of bands within each segment.

Computes GLCM texture metrics within each image segment.

Computes topological relations between segments, such as contact, containment, and adjacency.

Identifies low-confidence segments from their classification probabilities.

Refines segment boundaries through iterations with a minimum object size.

Merges or adjusts segments smaller than the defined minimum mapping unit.

Converts polygons into a segment raster at the specified cell size.

Transfers labels from parent to child segments using the object hierarchy.

Segments images with the Felzenszwalb method using scale, smoothing, and minimum area.

Generates coarse and fine segments forming a multiresolution object hierarchy.

Evaluates candidate segmentation scales and selects suitable parameters for the image.

Segments an image into compact superpixels controlling size, compactness, and minimum area.

Segments an image via marker-controlled watershed with gradient weighting.

Merges small segmented regions based on selected minimum size and method.

Splits segments with low internal cohesion at the specified cut scale.

Converts a segment raster into polygons while retaining the selected field.

Applies BRDF normalization to an image.

Corrects edge darkening in an image to homogenize brightness.

Applies dark object subtraction atmospheric correction to an image.

Converts digital numbers to top-of-atmosphere reflectance for an image.

Performs optical analytics with terrain correction; supports radiometric correction tasks.

Applies Cloude-Pottier decomposition to an image.

Applies an enhanced Lee filter to an image and outputs a filtered raster.

Applies Freeman-Durden decomposition to an image.

Applies a Frost filter to an image and outputs a filtered raster.

Applies a Gamma MAP filter to an image and outputs a filtered raster.

Applies H-alpha Wishart classification to an image.

Applies a Kuan filter to an image and outputs a filtered raster.

Applies a refined Lee filter to an image and outputs a filtered raster.

Applies Wishart iterative clustering to an image.

Applies Yamaguchi four-component decomposition to an image.

Normalizes image spectra against their continuum to highlight absorption features.

Applies linear spectral unmixing to an image.

Transforms multispectral bands to concentrate signal and separate noise-dominated components.

Applies the Spectral Angle Mapper method to an image.

Applies spectral library matching to an image.

Estimates land surface temperature from a single thermal band.

Estimates land surface temperature by combining two thermal bands.

Estimates surface emissivity from NDVI.