**MultiSpec© -- A Multispectral image data analysis system**

MultiSpec (©Purdue Research Foundation) is a processing system for
interactively analyzing Earth observational multispectral image data
such as that produced by the Landsat series of Earth satellites and
hyperspectral image data such as from current and future airborne and
spaceborne systems such as AVIRIS. MultiSpec satisfies the following
design goals:

> • The implementation should be on a readily available platform which
> has adequate processing power, but is financially within the reach of
> any Earth science researcher (i.e., ≤ \$5000).
>
> • The system should be easy to learn and easy to use, even for the
> infrequent user, using the most modern of software environments.
>
> • The system should provide for easy import of data in a variety of
> formats, and easy export of results, both in thematic map and in
> tabular form.

The work of building the current capability began by implementing an
upgraded version of the LARSYS multispectral image data analysis system.
LARSYS is one of the first remote sensing multispectral data processing
systems, originally created during the 1960\'s. A number of systems in
government laboratories, university research labs, and several
commercially offered products are descendants of LARSYS. The current
system, now called MultiSpec, has been implemented for the Apple
Macintosh personal workstation. Versions are available for 680x0 based
machines both with and without a math coprocessor and for PowerPC based
machines in native code. A color display capability with at least an 8
bit color range is highly desirable but not necessary; a 24 bit color
system provides even more attractive performance. A system configuration
using a Power Macintosh 8100/80 with 16 MB RAM and 500 MB hard disk, and
color display, would have a street price of about \$4000 at the present
time. Such a system is capable of classifying in excess of 2,400,000
pixel-classes per minute using 12 bands and a Gaussian maximum
likelihood scheme, about 10 times faster than a Quadra 840av. Given the
current cost/performance trends, even more cost-effective systems are
likely to be available in the future. MAE, a product enabling Macintosh
applications such as MultiSpec to run on Sun and HP workstations is
available, however, there is a significant processing speed penalty with
this system.

New capabilities are continually added to MultiSpec as they emerge from
our research on hyperspectral processing. Capabilities of the current
version of MultiSpec (announced 2/15/95) include the following.

> • **Import data** in either Binary or ASCII format with or without a
> header, and in Band Interleaved by Line (BIL), Band Sequential (BSQ),
> or Band Interleaved by Sample (BIS) formats. The data may have either
> one or two bytes per data value, and may have 4 to 16 bits per data
> value. In the case of two bytes per sample, the two bytes may be in
> either order.
>
> • **Display multispectral images** in a variety of B/W or color
> formats using linear or equal area gray scales; display (internally
> generated) thematic images also in B/W or color, with an ability to
> control the color used for each theme.
>
> • **Histogram** data for use in determining the gray scale regime for
> a display or for listing and graphing.
>
> • **Reformat** the data file in a number of ways, e.g., by adding a
> standard header, changing from any one of the three interleave formats
> to either of the other two, editing out channels, combining files,
> adding or modifying channel descriptions, mosaicing data sets,
> changing the geometry of a data set, and a number of other changes.
>
> • **Create new channels** of data from existing channels. The new
> channels may be the result of a principal components or feature
> extraction transformation of the existing ones, or they may result
> from the ratio of a linear combination of existing bands divided by a
> different linear combination of bands.
>
> • **Cluster** data using either a single pass or an iterative
> (isodata) clustering algorithm. Save the results for display as a
> thematic map. Cluster statistics can also be saved as class
> statistics. Use of clustering followed by ECHO spectral/spatial
> classification provides an effective multivariate scene segmentation
> scheme.
>
> • **Define classes** via designating rectangular or polygonal training
> fields, compute field and class statistics, and define test fields for
> use in evaluating classification results quantitatively. A feature
> called "Enhance Statistics" also allows one to improve the extent to
> which the defined class statistics fit the composite of all data in
> the data set.
>
> • **Determine** the **best** subset of **spectral features** to use
> for a given classification using (a) any of four statistical distance
> measures or (b) a new method based directly upon decision boundaries
> defined by training samples, or (c) a second method based directly
> upon the discriminant functions. Also included are methods especially
> designed to search for narrow spectral features such as spectroscopic
> characteristics.
>
> • **Classify** a designated area in the data file. Four different
> classification algorithms are available: use of minimum L1 or L2
> distance, the maximum likelihood pixel scheme, or the ECHO
> spectral/spatial classifier. Save the results for display as a
> thematic map, with or without training and test fields being shown.
> Apply a threshold to a classification, and generate a probability map
> showing the degree of membership of each pixel to the class to which
> it was assigned.
>
> • **List classification results** of training or test areas in tabular
> form on a per field, per class, or groups of classes basis.
>
> • Show a **graph** of the **spectral values** of a currently selected
> pixel or the mean ± σ for a selected area. Show **scatter diagrams**
> of data from pairs of bands and ellipses of concentration for training
> sets and selected areas. Show the coordinates of a currently selected
> area.
>
> • Show a **color presentation** of the **correlation matrix** for a
> field or class.
>
> • Several additional **utility functions** including listing out a
> subset of the data e.g., for use externally, conducting principal
> component analysis, etc.
>
> • **Transfer** intermediate or final **results**, be they text, B/W
> image or color image, to other application programs such as word
> processors, spreadsheet, or graphics program by copying and pasting or
> by saving and then opening the saved file within another application.

The MultiSpec implementation is carried out in such a way that
essentially the only limits on the number of lines or columns of the
data, the number of spectral bands, etc., are those determined by the
available disk and memory space. Taken together, these capabilities
provide a state-of-the-art capability to analyze moderate and high
dimensional multispectral data sets of practical size. It, along with a
80+ page document listing its capabilities in more detail and providing
tutorial exercises in its use, is available on request. Additional
documentation is available via the World Wide Web at the following URL:

http://dynamo.ecn.purdue.edu/\~biehl/MultiSpec/