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The Big List Of Satellite/Aerial Imagery Analysis Software II – I Through M

Part two of the series (part one here):


NASA Image2000 is being developed by NASA Goddard Space Flight Center Code 588 and NASA’s Scientific and Educational Endeavors (SEE). The purpose of NASA Image2000 is to provide a host-independent image processing system for students and educators using tutorials developed by SEE and the Center for Image Processing in Education (CIPE).

Note: If you can’t get the software from the main page, try this link.


Java-based image acquisition and analysis software. More of a general image analysis program, but with dozens of plug-ins for image enhancement and analysis.


The Integrated System for Imagers and Spectrometers (Isis) is a specialized image processing package. It has many standard image processing operations such as contrast stretch, image algebra, filters, and statistical analysis. Isis operates on both classical two-dimensional images as well as three-dimensional cubes collected from imaging spectrometers.

Note: Created by the USGS primarily for analyzing imagery from planetary probes.


IVICS (Interactive Visualizer and Image Classifier for Satellites) was developed as a visualization tool to facilitate selection of training samples from satellite images. It has evolved into a general purpose visualization system which supports several common satellite and remote sensing data formats.


JMicroVision was designed to describe, measure, quantify and classify components of all kinds of images. It has an intuitive user interface with powerful features and supports very large images (more than 1 GB, even with a computer with little memory). JMicroVision contains tools having various degrees of automation in order to handle with complex and varied images.

  • Read images in TIFF, BMP, FlashPiX, GIF, JPEG, PNG, and PNM formats
  • Efficient visualization system
  • Quantify components: objects or background
  • Object analysis (size, shape, orientation, texture …)
  • Object classification
  • Image processing (binary and morphology operations, filtering, segmentation…)
  • Image rectification (geometric corrections by control points)
  • Digital point counting
  • Tools for data collection in one or two dimensions
  • Image annotation and description card
  • Profile (variation of granulometry, density, objects or background)
  • Save all measures, data, calibration and preferences in a single project file

Note: Not primarily a geographically-oriented application


MicroMSI is a multi-spectral imagery analysis program that is optimized for commonly available microcomputer hardware (Windows).

    • Multiple display of multi-spectral imagery including: gray-level, multiband (pseudo-color and derived panchromatic), band-ratioed, band-differenced, thermal, NDVI, supervised classification (three algorithms), unsupervised classification, spectral classification, stereo anaglyph and principal component analysis.
    • Image data importing/indexing wizard that simplifies the process of accessing new imagery
    • MicroMSI supports band sequential, BIL and BSQ file in many common commercial image formats
    • Up to 256 bands per image allows access to hyperspectral data.
    • Geo-registration and geo-rectification of images
    • Image annotation with text, grids, north arrow.

Note: Active development ceased a few years ago. Link at NGA is currently dead, and I can’t find an alternate download site; when I do, I’ll update the link.

MSphinx – “The concept behind Msphinx is to develop a progressive system architecture for future satellite sensors that is completely independent of the data volume, size and format derived from satellite observations, without developping a complex internal data structure that will loose the particularity of satellite data : series of pixels forming rows or columns of an image.”


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 from current and future airborne and spaceborne systems such as AVIRIS.

  • 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 values may be 8-bit integer, 16-bit integer, 32-bit integer, 32-bit real or 64-bit real. In cases of two, four or eight bytes per sample, the 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. ArcView Shape Files may be overlain on the images.
  • 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 or mask image files, 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. A covariance estimation scheme (LOOC) can optimize that estimate for small training sets.
  • Determine the best spectral features to use for a given classification using (a) searching for the best subset of features using any of five statistical distance measures, (b) a 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, and for use of projection pursuit as a means of further improving the features extracted.
  • Classify a designated area in the data file. Six different classification algorithms are available: use of minimum distance to means, correlation classifier (SAM), matched filter (CEM), Fisher linear discriminant, the Gaussian 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/threshold 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 ± s for a selected area. Show scatter diagrams of data from pairs of bands and ellipses of concentration for training sets and selected areas. Show a graph of the histograms of the class or field data values used for training. Show the coordinates of a currently selected area.
  • Show a color presentation of the correlation matrix for a field or class as a visualization tool especially for hyperspectral data.
  • 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.

Note: Extensive downloadable documentation and sample data files.

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2 Responses to “The Big List Of Satellite/Aerial Imagery Analysis Software II – I Through M”

  1. 1 The Big List Of Satellite/Aerial Imagery Analysis Software IV – S Through Z | Free Geography Tools
  2. 2 The Big List Of Satellite/Aerial Imagery Analysis Software III – N Through R | Free Geography Tools
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