NTA-NET (UGC-NET) Geography (80) Cartography-Digital Image Processing, Remote Sensing and Big Data Study Material (Page 5 of 11)

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Supervised Classification

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Introduction

  • The purpose of Image classification is to categorize all pixels in a digital image into different land use/land cover classes. Depending on the interaction between computer and interpreter during classification process, there are two types of classification. These two main categories used to achieve classified output are called Supervised and Unsupervised Classification techniques.
  • Supervised classification is based on the idea that a user can select sample pixels in an image thatare representative of specific classes and then direct the image processing software to use thesetraining sites as references for the classification of all other pixels in the image. Many analysts use a combination of supervised and u…

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Appropriate Classification Algorithm

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Selection of Appropriate Classification Algorithm

Various supervised classification algorithms may be used to assign an unknown pixel to one of a few classes. The choice of a particular classifier or decision rule depends on the nature of the input data and the desired output. Parametric classification algorithm assumes that the observed measurement Xe for reach class in each spectral band during the training phase of the supervised classification are Gaussian in nature i.e.. they are normally distributed. Non parametric classification algorithm makes no such assumptions.

Selection of Appropriate Classification Algorithm

Parallelepiped Classification Algorithm

This is a widely used decision rule based on simple Boolea…

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