BlueHat is a collection of python routines to facilitate the analysis of spectral (multispectral and hyperspectral) remote sensing imagery, with an emphasis on various kinds of target detection. The package provides collections of utilities that developers can use in producing algorithms for specific target detection scenarios. In particular, this package was used to develop the algorithms used by the on-board software in the NACHOS project.
The Grande Visione is an all-encompassing multi-author open-source package that does all kinds of spectral image processing, focused mainly on target and anomaly (and anomalous change) detection. There is less emphasis on endmembers, segmentation, etc -- though the package might include basic endmember or segmentation routines if they are useful for target detection. In this grander vision, machine learning might play a sizable role (eg, with tools for matched pair machine learning).
A nearer-term vision is a kind of 'util' directory that enables different target detection projects to use common 'standard' processing routines. (I'm thinking: covariance estimation, principal components and whitening, various matched filters, ROC [receiver operating characteristic] curve utilities, etc.) The minimal aim here is just to avoid all that cutting-and-pasting, and copying and overwriting, and non-propagating updates that are inevitable when multiple projects are independently maintained.
BlueHat does not really mean anything at all. The name refers to a graphic that describes a particular approach for local background estimation in multispectral imagery. A pixel under test (indicated in blue) is estimated as a function of the pixels (indicated in pink) in an annulus surrounding that pixel. The "hat" indicates that it is an estimate.
Although bluehat was not chosen to be an acronym, you can perhaps think of it as Blue Hyperspectral Analysis Tools. And you can perhaps think of "blue" as "Best Let U Experiment" -- since the aim of this software is not to furnish ultimate end-users with canned target detectors, but to provide algorithm developers with utilities that empower them to create their own designs and to perform their own experiments. Which is to say: this software is designed primarily for me, and secondarily for people like me.
Second place, by the way, in terms of giving this package a name, was Some Utilities for Spectral Image Exploitation and Quantification with the acronym SUSIEQ. Third place was Local/Global Utilites for Spectral Imagery with the acronym LUSI/GUSI. If you are looking to write your own spectral image analysis software package, these names are both available.
An overall design consideration is the extent to which the package is more 'matlab-like' in terms of providing basic utilities with generic interfaces, or more 'pythonic' with data structures that take advantage of python's object-oriented style. For instance, should a data cube "know" which dimension is its spectral dimension? (In other words, should the "interleave" of a data cube be part of the data structure?)
In general, I have tried to find a balance that employs generic data structures (mostly numpy arrays) for the basic objects (such as data cubes or target spectra) that a user will want to manipulate, while employing more sophisticated objects "under the hood" for some of the intermediate processing (such as whitening). The idea is be as helpful as possible to the developer without making that developer totally "buy in" to the bluehat system. (All hats are welcome!) [On the other hand, I have totally bought into the bluehat system myself, so this compromise may have some ragged edges...]
Having said all that, a bluehat image cube does know its interleave type, but not because it is incorporated into a higher-level data object, rather because the convention is enforced that all image cubes are of the BIP form.
Although ENVI format is a kind of a standard in remote sensing (and GeoTIFF is another "kind of standard"), the bluehat file format of choice is HDF5, which allows multiple datasets, and arbitrary metadata, all in a single file. And especially large files can be set up to be read in efficient incremental ways.
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basic: basic spectral routines, means and covariances, and infrastructure for dealing with different interleave options (BIP, BIL, BSQ). The standard/default interleave for this package is BIP -- "Band Interleaved by Pixel" -- that is: the image cube is encoded into a numpy array as [row,sample,band].
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whiten: routines for principal components and data whitening
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smt: sparse matrix transform (for approximating convariance matrices)
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rocu: utilities for ROC curves
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densitymodels: implements density models for Gaussian, multivariate t, and anisotropic fat exponential distributions.
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ecu: utilities for elliptically-contoured (EC) distributions, primarily the multivariate t distribution.
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fatexp: utilities for anisotropic fat exponentials
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bkgest: background estimation utilities, including the local regression framework, in which a pixel's background is estimated from the annulus of pixels that surround it.
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ioutils: routines for reading and writing data cubes, target spectra, and truth maps from HDF5 data files; some rudimentary routines for reading ENVI files.
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endmember: (still mostly untested) routines for simple endmember and unmixing analysis
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verbose: generic utilities for writing informational and/or warning messages conditioned on a user-specified level of verbosity.
Spectral Python (SPy) is an excellent package, but bluehat is mostly independent of SPy (bluehat currently calls SPy's ENVI reading libraries, and SPy is totally independent of bluehat). If you are thinking about using bluehat for the first time, you should check out SPy as well, to see which is more useful for your purposes. I have tried to employ more generic data structures in bluehat and I have included utilities that I have found useful in my own research, and these often differ from what is available in SPy.
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This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.
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