By John B. Adams
This quantity describes the right way to strategy and interpret spectral pictures utilizing actual versions to bridge the distance among the engineering and theoretical aspects of remote-sensing and the outside global. Examples are drawn from quite a few landscapes and interpretations are demonstrated opposed to the truth obvious at the flooring. The reader is led via research of actual photographs (using figures and causes) and examples are selected to demonstrate vital features of the analytic framework. The e-book is supplemented via a web-site website hosting electronic colour types of figures within the publication in addition to ancillary pictures (www.cambridge.org/9780521662214).
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Extra info for Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach (Topics in Remote Sensing)
Our objectives may be as general as finding out all we can about an area on the ground – a reconnaissance – or they may involve very specific goals. In either case, the objectives and the requirements for accuracy will determine how to carry out the analysis of an image and how to validate the results. Expand from small to larger areas. Skilled image analysts often spend much of their time on one or more small subsets of a larger image in order to acquire key information about the ground surface.
Consider, for example, that our target crop (wheat) has a spectral signature nearly identical to another crop grown in the area – barley, perhaps. Our spectral analysis would locate all of the crop fields that could be either wheat or barley. However, if the two crops were planted, matured and harvested at different times, and if we had a time series of images, we could resolve the ambiguity. The time series could consist of just two images, providing that they were obtained when differences between the two crops were evident.
5 Testing and validating results that are external, such as atmospheric and topographic effects. 2). Fortunately, images from many of the standard sources already have been corrected for the most obvious internal geometric and radiometric errors by the time they reach the analyst. External errors usually are the most difficult to fix, because they were produced at the moment of data acquisition, and it is unlikely that anyone was on the ground at that time making all the needed measurements of the true conditions.