Strictly, then, spectral measurements involve interactions between illuminating radiation and the atomic/molecular structures of any material, leading to a reflected signal, followed by some further changes to this signal as it returns through the atmosphere, and finally, depend on the nature of the response of the detector system(s) in the sensor. However, in practice objects and features on the Earth's surface are described more as classes than as materials per se. Consider, for instance, the material "concrete". It may be found in roadways, parking lots, swimming pools, buildings, and other structural units, each of which might be treated as a separate class. Vegetation can be subdivided in a variety of ways: trees, crops, grasslands, lake bloom algae, etc.; finer subdivisions are permissable, by classifying trees as deciduous or evergreen, or deciduous trees into oak, maple, hickory, poplar, etc.

These various classes, some of which can share the same materials, are distinguished by two additional properties beside their spectral attributes, namely, shape (geometric patterns) and use or context (sometimes including geographical locations). Thus, a feature composed of concrete may be assigned to the classes 'streets' and 'parking lots' depending on whether its shape is linear or more or less equant. Two features with nearly identical spectral signatures for vegetation might be assigned to the classes 'forest' and 'crops' depending on whether their areas of similar spectral response, as seen in an image, have irregular or straight (often rectangular) boundaries. A chief use of remote sensing data is in classifying the myriad of features in a scene (usually presented as an image) into meaningful categories or classes that can be converted to a thematic map (the theme is selectable, e.g., land use; geology; vegetation types; rainfall). How this is done using an aerial or space image is surveyed in the tutorial that accompanies Section 1. As a preview: an unsupervised classification results when features are separated solely on their spectral properties and a supervised classification is made when some prior or acquired knowledge of the classes present in parts of the scene is utilized in setting up training sites to estimate and identify the spectral characteristics of each class.

The task of any remote sensing system is simply to detect radiation signals, determine their spectral character, derive appropriate signatures, and interrelate the spatial positions of the classes they represent. This ultimately leads to some type of interpretable display product, be it an image or a map or a numerical data set, that mirrors the reality of the surface (or some property[ies] within the atmosphere) in terms of the nature and distribution of the features present in the field of view.

(As an aside comment at this point: You will become increasingly aware as you work through the Sections comprising this Tutorial that color is often an essential "ingredient" in most remote sensing images. While variations in black and white imagery can be very informative - and were the norm in the earlier aerial photographs - the number of different gray tones separable by the eye is limited to about 20-30 (out of a maximum of ~200) steps on a contrast scale. By contrast, the eye can pick out and distinguish 20000 or more color tints, so that small but often important variations related to real differences within the target materials or classes can be discerned. Liberal use of color in the illustrations found throughout the Tutorial takes advantage of this capability; unlike most textbooks, in which color is restricted owing to costs, Web Sites on the Internet and CD-ROMs - both being the media of choice for this Tutorial - are not burdened by this limitation. For a comprehensive review of how the human eye functions to perceive gray and color levels, consult Chapter 2 in Drury, S.A., Image Interpretation in Geology, 1987, Allen & Unwin)

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