Distance functions
This module supports functions to calculate the distance between two vectors.
chebychev
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distance.chebyshev(s1, s2)
Computes the chebychev distance between two vector.
- Parameters:
- s1: numpy array
- The first vector.
- s2: numpy array
- The second vector.
- Returns: float
- Chebychev distance between s1 and s2.
NormXCorr
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distance.NormXCorr(s1, s2)
Computes the normalized cross correlation distance between two vector.
- Parameters:
- s1: numpy array
- The first vector.
- s2: numpy array
- The second vector.
- Returns: float
- NormXCorr distance between s1 and s2, dist is between [-1, 1].
A value of one indicate a perfect match.
SAM
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distance.SAM(s1, s2)
Computes the spectral angle mapper between two vectors (in radians).
- Parameters:
- s1: numpy array
- The first vector.
- s2: numpy array
- The second vector.
- Returns: float
- The angle between vectors s1 and s2 in radians.
SID
-
distance.SID(s1, s2)
Computes the spectral information divergence between two vectors.
- Parameters:
- s1: numpy array
- The first vector.
- s2: numpy array
- The second vector.
- Returns: float
- Spectral information divergence between s1 and s2.
- Reference
- C.-I. Chang, “An Information-Theoretic Approach to SpectralVariability,
Similarity, and Discrimination for Hyperspectral Image”
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 46, NO. 5, AUGUST 2000.