We propose a quantum version of the well known minimum distance classification model called Nearest Mean Classifier (NMC). In this regard. Minimum Distance Classifier. Training. The kth class $\omega_k$ is represented by its mean vector ${\bf m}_k$ and covariance matrix which can be estimated. The utilization of minimum distance classification methods in remote sensing problems, such as crop species identification, is considered. Minimum distance.


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The deciduous forests are represented mainly by small-scale floodplain forests on the left bank of the Donets and the broad-leaved tract of Tyundik on the right bank.

Supervised image classification using minimum distance algorithm

Coniferous forests are Andreevsky Birch, which grows on the left-bank terrain of the Donets, between its floodplain and Lake Lyman. The bands cover the minimum distance classifier, red and infrared parts of the spectrum.

Figure 2 shows a false color composite of the band combination infrared — red — green. This composite shows the conifers as brown, the deciduous trees as bright red.


The water bodies appear as black minimum distance classifier dark blue. When analyzing the posilions of the ROI pixels in the n-D feature space, we see that they overlap fig. That is why this minimum distance classifier we should use the minimum distance algorithm for our classification. Training regions in the 3-dimensional spectral feature space So, we have made sure that minimum distance is the right algorithm.

Next, we will go through the process step by step.

Minimum Distance Classifier | Kaggle

Classification Input File window appears. Select the image that needs to be classified. The settings window for the minimum distance algorithm classification has a similar interface to the one for parallelepiped algorithm.

It also has four blocks: Setting for saving rule images Output Rule Images? The only difference is the parameter that sets the boundaries of the classes.

In particular, the classification error provided by the QNMC changes by feature rescaling. As a consequence, we observe that, for several datasets, the new encoding exhibits a further advantage that can be gained by exploiting the non-invariance under rescaling, and also for n-dimensional problems minimum distance classifier to the previous works.

To this end, some experimental results have been presented.

Minimum distance

The organization of this paper is as follows. In Section 2the minimum distance classifier process and the formal structure of the NMC for multi-class problems are described. Section 3 is devoted to the definition of a new encoding of real patterns into quantum states.


Specifically in minimum distance classification a sample i. The measure of resemblance is a distance measure in the space of distribution functions.

The literature concerning both minimum distance classification problems and distance measures is reviewed. For all other, please allow us to give a definition: Definition of the Distance on a Euklidean Space minimum distance classifier is an affine vektor space with a valid scalar product dot product defined on:

  • Supervised image classification using minimum distance algorithm
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  • "Minimum Distance Classification in Remote Sensing" by A. G. Wacker and D. A. Landgrebe
  • Classification Algorithms and Methods
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