## 2 Non Destructive Sensing
### Algorithms
This control is essential if it is the specific task to compare one classification with another of the same scene at different dates, or if the classification must be compatible with those of adjacent regions. Serious classification errors are detectable by field verification to determine whether they have been correctly classified. Supervised classification has numerous disadvantages, as the analyst imposes a classification structure upon the data based on predefined classes instead of finding natural classes in an image. In supervised classification, training sites and classes are based primarily on the information categories and only secondarily on spectral properties. Another source of error is the selection of training data, since these samples of pixels may not be representative of conditions encountered throughout the image. Moreover, supervised classification is not able to recognize the specific or unique categories which are not represented in training data due to the small areas they occupy on the image or simply because they are not known to the analyst. Unsupervised classification involves the process of automatically segmenting an image into spectral classes based on the natural groupings found within the data set. Multi-band spectral response patterns are grouped into clusters which are statically separable. In supervised classification, any individual pixel is compared to each discrete cluster to select the one which is closest in terms of spectral values. The two most frequently used grouping algorithms are K-means and ISODATA cluster algorithms, which are iterative procedures. Three advantages of unsupervised classification are: no extensive prior knowledge of the region of interest is required; the opportunity of human error is minimized; unique classes are recognized as distinct units in unsupervised classification. Since unsupervised classification identifies spectrally homogenous classes within the data, such classes do not necessarily correspond to the informational categories which are of interest to the analyst. Unlike supervised and unsupervised image classification, SMA did not rely on the detection or identification of pixel clusters with similar reflectance spectra. Rather, it is possible to consider each pixel individually and assess the presence and proportion of selected end-members. The fraction images produced by SMA refer to a pixel- by pixel measure of the percentage composition of each end-member in the spectral mixing model. The SMA technique is able to generate more accurate estimates of the end-member classes and appeared to be an effective means of mapping vegetation cover. Since supervised and unsupervised methods are based on predefined classification schemesclassifying entire pixels, this causes an error which often produces too high or low estimates of land cover classes due to the inability to distinguish sub-pixel covers. From these facts it is clear that the application of SMA and the production of end-members fraction images for land cover classification allow for a more detailed analysis of individual pixels in the image. Thus, it can maintain higher accuracy in classification and provide more realistic representation of landscape, as opposed to the patchy and discrete nature of traditional classification techniques. Change Vector Analysis (CVA) is an effective approach for detecting and characterizing land cover change. Processing and analyzing is applied to multi-spectral/multi-temporal data layers [126, 127]. The vector describing the direction and magnitude of change from the first to second date is a spectral change vector. This time trajectory is represented as a vector in multidimensional measurement space. The length of the change vector indicates the magnitude of change, while its direction indicates the nature of the change [128].