Class/Function Glossary¶

File Input/Output¶

Class/Function	Description
`EcostressDatabase`	Create/Query a spectral database generated from the ECOSTRESS Spectral Library
`aviris.open`	Open an AVIRIS image file
`aviris.read_aviris_bands`	Read an AVIRIS band calibration file
`envi.open`	Open a data file (image, classification, or spectral library) that has an ENVI header
`envi.create_image`	Open a new (empty) image file with an ENVI header
`envi.save_classification`	Save classification labels to a file with a corresponding ENVI header
`envi.save_image`	Save image data to a file with a corresponding ENVI header
`envi.SpectralLibrary`	Class to create/save spectral libraries in ENVI format
`erdas.open`	Open an Erdas LAN image file
`open_image`	Generic function for opening multiple hyperspectral image file formats

Class/Function	Description
`ColorScale`	Create color scales for use with data display functions
`get_rgb`	Produce RGB array suitable for display from image data
`ImageView`	Class for interacting with image displays (returned by `imshow`)
`imshow`	Primary function for displaying raster views of image data (an extension of matplotlib’s imshow providing overlays and interactivity)
`ppi`	Display Pixel Purity Index values while `ppi` function executes.
`save_rgb`	Saves image data in common RGB image formats (e.g., JPEG, PNG)
`view_cube`	View an interactive 3D image cube
`view_nd`	Display an interactive N-D visualization of image pixel data

Class/Function	Description
`linear_discriminant`	Computes Fisher’s Linear Discriminant for a set of classes
`mnf`	Minimum Noise Fraction
`ppi`	Calculates Pixel Purity Index (PPI)
`principal_components`	Calculates principal components of an image

Class/Function	Description
`ace`	Adaptive Coherence/Cosine Estimator
`matched_filter`	Applies a linear matched filter detector for a given target
`msam`	Modified Spectral Angle Mapper (MSAM)
`rx`	RX anomaly detector
`spectral_angles`	Spectral Angle Mapper (SAM)

Class/Function	Description
`create_training_classes`	Creates a `TrainingClassSet` object from image data and ground truth
`GaussianClassifier`	Gaussian Maximum Likelihood Classifier (GMLC)
`kmeans`	Unsupervised image classification via k-means clustering
`MahalanobisDistanceClassifier`	A classifier using Mahalanobis distance
`map_class_ids`	Create a mapping between class labels in two classification images
`map_classes`	Modifies class indices according to a class index mapping
`msam`	Modified Spectral Angle Mapper (MSAM)
`PerceptronClassifier`	A Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) classifier
`spectral_angles`	Spectral Angle Mapper (SAM)
`TrainingClassSet`	Object returned from `spectral.create_training_classes` (used by some classifiers)

Class/Function	Description
`BandInfo`	Container for spectral band discretization/calibration data
`BandResampler`	Class for performing band resampling
`FisherLinearDiscriminant`	Object returned by `linear_discriminant` to transform image data into linear discriminant space.
`LinearTransform`	A callable linear transform that can be applied to image data
`MNFResult`	Object returned by `mnf` to reduce dimensionality via Minimum Noise Fraction (MNF)
`ndvi`	Normalized Difference Vegetation Index (NDVI)
`PrincipalComponents`	Object returned by `principal_components`. Transforms data into PCA space and - optionally - reduces dimensionality.

Class/Function	Description
`bdist`	Bhattacharyya distance
`calc_stats`	Calculates Gaussian statistics for image data
`covariance`	Calculates image data covariance
`cov_avg`	Calculates covariance averaged over a set of ground truth classes
`noise_from_diffs`	Estimate image noise statistics from a homogeneous region
`orthogonalize`	Performs Gram-Schmidt orthogonalization on a set of vectors
`principal_components`	Calculates principal components of an image
`transform_image`	Applies a linear transform to image data

Class/Function	Description
`settings`	Object for controlling SPy configuration options
`algorithms.algorithms.iterator`	Function returning an iterator over image pixels