Once the clusters are defined, you would apply a classification algorithm to sort the remaining pixels in the scene into the class with the most similar spectral characteristics. If you did a good job in supervising the training stage of the classification, each cluster would represent the range of spectral characteristics exhibited by its corresponding land cover class. The training fields you defined consist of clusters of pixels with similar reflectance or emittance values.
#MULTISPEC INC SOFTWARE#
Multispec image processing software © 2001 Purdue Research Foundation, Inc. In supervised classification, the analyst's role is to specify in advance the multispectral reflectance or (in the case of the thermal infrared band) emittance values typical of each land use or land cover class.įigure 8.18.2 Training fields defined for two classes of land cover, vegetation and other.
Human image analysts play crucial roles in both supervised and unsupervised image classification procedures. Two general types of image classification techniques have been developed: supervised and unsupervised techniques. To realize this potential, image analysts have developed a family of image classification techniques that automatically sort pixels with similar multispectral reflectance values into clusters that, ideally, correspond to functional land use and land cover categories. Part of the appeal of digital image processing is the potential to automate land use and land cover mapping. It's not surprising, then, that the Commission appointed in 1949 failed in its attempt to produce a detailed global land use map. As you might imagine, this was an expensive, time-consuming process. Prior to the era of digital image processing, specially trained personnel drew land use maps by visually interpreting the shape, size, pattern, tone, texture, and shadows cast by features shown in aerial photographs. For instance, an early classification system adopted by a World Land Use Commission in 1949 consisted of nine primary categories, including settlements and associated non-agricultural lands, horticulture, tree and other perennial crops, cropland, improved permanent pasture, unimproved grazing land, woodlands, swamps and marshes, and unproductive land. Land use, by contrast, refers to the functional roles that the land plays in human economic activities (Campbell, 1983).īoth land use and land cover are specified in terms of generalized categories. The term land cover refers to the kinds of vegetation that blanket the Earth's surface, or the kinds of materials that form the surface where vegetation is absent. Along with military surveillance and weather forecasting, a common use of remotely sensed image data is to monitor land cover and to inform land use planning.
Also, Anti-X-Talk™ technology enables the use and interchange of a wide range of lens types on the same camera and allows better light collection efficiency since filter response is insensitive to large aperture settings (e.g., f/2.8). Furthermore, high quality image data from the MSC and MSC2 cameras can be used as is without the need for proprietary post-processing algorithms. The result is multispectral images with better spectral discrimination and higher contrast.
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It works by blocking stray light between adjacent filters, so the pixel response is predictable and directly related to the actual spectral response of the overlying pixelated filter. Spectral Devices invented Anti-X-Talk™ technology to overcome these problems. Without Anti-X-Talk™ technology, images suffer from low contrast and spectral ambiguity. Without Anti-X-Talk™ technology, stray light between spectral channels is significant, often exceeding the light leakage due to spectral overlap between adjacent filters. Anti-X-Talk™ technology works at the filter level and prevents light leakage between individual filters. Anti-X-Talk™ technology is unique to Spectral Devices multispectral cameras.
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