This page considers methods involved in interpreting spectral data that can be used to define and separate - thus classify - materials, objects, and features. The role of both spatial characteristics and distinctive colors in making classifications is discussed. The two principal modes of classification - unsupervised and supervised - are described. The notion of ground truth is introduced, as is a quick reference to sensor types used to acquire the data to be analyzed.

### Interpretation and Classification of Remotely Sensed Data

This part of the Introduction, which centers on principles and theory underlying the practice of remote sensing, closes with several guidelines on how images (or the numerical data used to make them) are interpreted and classified (a preview of a more extended treatment in Section 1).

In the sets of spectral curves shown below (made on site using a portable field spectrometer), it is clear that the spectral response for those vegetation types is distinct from common inorganic materials. The reflectance for vegetation rises abruptly at about 0.7 µm, followed by a gradual drop at about 1.1 µm. The first (left or top) spectral signatures indicate a gradual rise in reflectance with increasing wavelengths for those particular common manmade materials on the ground. Concrete, being light-colored and bright, has a notably higher average than dark asphalt. The other materials fall in between. The shingles are probably bluish, in color as suggested by a rise in reflectance from about 0.4 to 0.5 µm and a flat response in the remainder of the visible (0.4 - 0.7 µm) light region. The second curves (on the right or bottom) indicate most vegetation types are very similar in response between 0.3 - 0.5 µm; show moderate variations in the 0.5 - 0.6 µm interval; and display maximum variability (hence optimum discrimination) in the 0.7 - 0.9 µm range.

I-15: At what one wavelength does there appear to be maximum separability of the five Non-vegetated Classes; the five Vegetated Classes? ANSWER

Making spectral measurements depends on the interactions between the incident radiation and the atomic and molecular structures of the material. These interactions lead to a reflected signal, which changes some as it returns through the atmosphere. Finally, the measurement depends on the nature of the detector system's response in the sensor. After testing the response of many materials, remote sensing experts can use spectral measurements to describe an object by its composition. In practice, we describe objects and features on Earth's surface more as classes than as materials per se. Consider, for instance, the material concrete. We us it in roadways, parking lots, swimming pools, buildings, and other structural units, each of which might be treated as a separate class. We can subdivide vegetation in a variety of ways: trees, crops, grasslands, lake bloom algae, etc. Finer subdivisions are permissible, by classifying trees as deciduous or evergreen, or deciduous trees into oak, maple, hickory, poplar, etc.

Two additional properties help to distinguish these various classes, some of which have the same materials; namely, shape (geometric patterns) and use or context (sometimes including geographical locations). Thus, we may assign a feature composed of concrete to the classes 'streets' and 'parking lots,' depending on whether its shape is long and narrow or more square or rectangular. Two features with nearly identical spectral signatures for vegetation, we may assign to the classes 'forest' and 'crops' depending on whether the area in the images has 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. The image then becomes a thematic map (the theme is selectable, e.g., land use; geology; vegetation types; rainfall). In Section 1 of the Tutorial we explain how to interpret an image using an aerial or space image to derive a thematic map. This is done by creating an unsupervised classification when features are separated solely on their spectral properties and a supervised classification when we use some prior or acquired knowledge of the classes in a scene 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, a map, or a numerical data set, that mirrors the reality of the surface (or some atmospheric property[ies]) in terms of the nature and distribution of the features present in the field of view.

Another essential ingredient in most remote sensing images is color. 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 that the eye can separate is limited to about 20-30 steps (out of a maximum of ~200) on a contrast scale. On the other hand, the eye can distinguish 20,000 or more color tints, so we can discern small but often important variations 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. 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.

Finally, we mention another topic that is integral to effective interpretation and classification. This is often cited as reference or ancillary data but is more commonly known as ground truth. Under this heading are various categories: maps and databases, test sites, field and laboratory measurements, and most importantly actual onsite visits to the areas being studied by remote sensing. This last has two main facets: 1) to identify what is there in terms of classes or materials so as to set up training sites for classification, and 2) to revisit parts of a classified image area to verify the accuracy of identification in places not visited. We will go into ground truth in more detail in the first half of Section 13; for a quick insight switch now to page 13-1.

Primary Author: Nicholas M. Short, Sr. email: nmshort@ptd.net