PIT was originally written in Unix but the program has been modified to run under Windows. The version you will download from the CD-ROM or from the Internet is designed to operate only in Microsoft Windows (95/98 or NT), i.e., is oriented towards PC systems. PIT currently can handle standard Band Sequential format which, for Landsat, expresses each image point in a DN range from 0-255 (single byte; 8 bits). Both Landsat MSS and TM are accepted, as is properly formatted AVHRR and GOES data; radar and hyperspectral data sets are not supported.

The data sets you will create presently cannot be saved under certain circumstances (exceptions will be noted). Thus, PIT is essentially a training tool for learning by doing the basics of image processing. It is not yet designed to yield permanent images that can be ported from the program to external directories for other uses (but watch for a .gif capability to be added). PIT can display a raw image, or a specially stretched single image representing some given band. This image enhancement is not through a program such as linear stretch or histogram equalization (described in Section 1), but by simply repositioning movable bars associated with Brightness (B) and Contrast (C) that appear when individual bands are displayed. Pit does not routinely allow for simultaneous display of multiple images, so that visual comparison must be carried out by creating or calling for one image at a time. PIT also permits color composites, using three input images assigned arbitrarily to the red, green, and blue monitor guns. These can be natural color (TM band 1,2,3), standard false color (2,3,4), or other color-band combinations (e.g, 7R, 5G, 1B). PIT is also capable of producing single ratio images in black and white, or colored ratio composites. It is likewise able to conduct Principal Components Analysis (PCA) generating as many Principal Components Images (PCI) as input bands. These also can be combined in groups of three to generate color composites.

PIT's primary use is in image classification. Both unsupervised and supervised programs are included. The three supervised methods are Maximum Likelihood (ML), Probablistic Neural Network (PNN), and Polynomial Discriminate Method (PDM) (the Minimum Distance Classifier is not included). Outputs are color-coded images with a legend of classes (no upper limit on number chosen but best confined to less than 20) exhibiting color assignments shown either on the top or the left of the classified image. Training sites for each class are selected by filling in some number of cells that appear as a grid over the image being interpreted; the cell size can be varied. Statistics on the class distribution are available.

Once chosen, the class DN values can be displayed as histograms or spectral signatures. Histograms for each input band can also be shown. A portion of a band histogram can be selected and then all pixels in the image having the DN values within that segment may be highlighted in color for those pixels within the image falling within this DN range. Scatter diagrams plotting the distribution of DN values for any two bands are producible.