Learning Texture Information from Singular Photographs and its Application in Digital Image Classification
An area which has rapidly gained interest in the last few years is that of remote sensing by imagery.1, 2 Certainly the successful mission of the ERTS has contributed to an expanding interest and increased potential.3 The potential uses of such remotely sensed data cover a variety of applications, including land use studies, crop quality, accurate map making, and evaluating natural resources. In utilizing this data in a particular application, a significant problem is the amount of data that must be processed to obtain specific information or features pertinent to the application. This is inherent in image or visual information; when this is multiplied by a large number of the available images, the problem of obtaining details or small features from such images is significant. This is particularly true when some set of features is to be used in a parametric pattern recognition scheme. However, there are many instances when one is interested in identifying an area or region which may encompass many details, but is small compared with an overall image and represents a defined entity. Then, if one is interested in identifying or classifying such a region, its gross characteristics must be represented while ignoring small details that may vary significantly from one sample observation to another.
KeywordsFeature Vector Training Sample Aerial Photograph Texture Information Texture Class
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