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Remote Sensing - a tool for environmental observation

Figure 5.14 rotated coordinate system used in a

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Unformatted text preview: Figure 5.14 Rotated coordinate system used in a principal component transformation (Lillesand & Kiefer, 1994). ────────────────────────────────────── TM1 TM2 TM3 TM4 TM5 TM7 ────────────────────────────────────── TC1: Brightness .3037 .2793 .4743 .5585 .5082 .1863 TC2: Greenness -.2848 -.2435 -.5436 .7243 .0840 -.1800 TC3: Wetness .1509 .1973 .3279 .3406 -.7112 -.4572 ────────────────────────────────────── Table 5.2 Thematic Mapper Tasselled Cap Transformation Coefficients proposed by Crist and Cicone (1984a). 78 Figure 5.15 Graphical representation of the Tasselled Cap Transformation (Schowengerdt, 1997). 5.9 Image classification Digital image classification refers to the process of assigning pixels to classes. Usually each pixel is treated as an individual unit composed of values in several spectral bands. Classification of each pixel is based on the match of the spectral signature of that pixel with a set of reference spectral signatures. The term ‘classifier’ refers loosely to a computer program that implements a specific procedure for image classification. The classes form regions on a map or an image, so that after classification the digital image is presented as a GIS-layer or a mosaic of uniform parcels each identified by a colour or symbol. Most classifiers are spectral classifiers or point classifiers because they consider each pixel as a point observation. Other methods of image clas- sification are based on textural information of the image, they use information from neighbouring pixels to assign classes to pixels and are referred to as context classifiers or textural classifiers. A basic distinction between classifiers separates supervised classification from unsupervised classification: In supervised classification (figure 5.16), the image analyst controls the pixel categorization process by specifying, to the classification algorithm, numerical descriptors of the various land cover types in an image. Representative sample sites of known cover type (called training areas or ground truth polygons ) are used to characterise land cover types in terms of average 79 reflectance values per spectral band and their variance. While classifying, each pixel in the image, is compared numerically to each category of land cover and labelled with the category, it is most similar. The success of the supervised classification depends on the capability of the analyst to define representative training areas. Some criteria for training area are: - the number of pixel per land cover type must be sufficient e.g. 100 pixels per land cover type; - the size of the training area should be sufficient large to include the spectral variance; - the training areas should be uniform with a statistically normal distribution and without outliers: the histogram of a training area should never display two or more distinct peaks,...
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Figure 5.14 Rotated coordinate system used in a principal...

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