In remote sensing, a mixed pixel (also called a mixed cell or mixel) is a pixel that contains more than one land-cover type inside its area.
This is very common when:
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spatial resolution is coarse (e.g., 30 m, 250 m)
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land cover is patchy or complex
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boundaries between features exist (forest–agriculture, land–water edges)
Traditional hard classification assigns each pixel to one single class only → either forest or soil or water.
But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors.
Two major solutions are:
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Spectral Mixture Analysis (SMA)
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Fuzzy Classification
1. Spectral Mixture Analysis (SMA)
(Also called Linear Spectral Unmixing)
✔ Concept
SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel.
These pure materials are called endmembers.
✔ Endmembers
Endmembers are spectrally pure classes found either from:
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field spectrometry
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image-derived signatures
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spectral libraries (USGS)
Common endmembers:
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vegetation
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soil
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water
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impervious surface
✔ What SMA does
Instead of assigning a pixel to a single class, SMA tells you:
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40% vegetation
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30% soil
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30% built-up
This is much more realistic for mixed pixels.
✔ Applications
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Urban mapping (impervious surface fraction)
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Forest canopy density
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Burn severity
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Soil–vegetation mixing analysis
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Water content in wetlands
✔ Key Terminology
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Endmember
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Linear mixture model (LMM)
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Fractional abundance
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Spectral unmixing
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Residual error (RMS error)
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Constrained unmixing (fractions = 0 to 1)
2. Fuzzy Classification
(Also called Soft Classification or Fuzzy Logic Classification)
✔ Concept
In fuzzy classification, each pixel has a degree of membership in multiple classes.
A pixel is not forced to belong to only one class.
Instead, it gets membership values between 0 and 1 for each class.
Example:
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Forest = 0.6
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Shrubland = 0.3
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Grassland = 0.1
This reflects the uncertainty and mixed nature of the pixel.
✔ Why fuzzy logic?
Because remote sensing pixels often contain mixtures of features and have vague boundaries.
✔ How it works
Fuzzy logic uses:
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Membership functions (Gaussian, linear, sigmoidal)
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Similarity measures
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Fuzzy sets
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Fuzzy rules (IF–THEN rules)
The output is a fuzzy membership map, not a single-class map.
✔ Advantages
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Represents uncertainty
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Better accuracy for heterogeneous landscapes
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Natural for ecological and land-cover gradients
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Useful for coarse resolution imagery (MODIS, AVHRR)
✔ Applications
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Land-cover classification
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Forest–nonforest transition zones
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Wetland mapping
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Urban–rural gradients
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Agricultural mosaics
✔ Key Terminology
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Fuzzy set
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Membership function
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Degree of belonging
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Soft classification
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Ambiguous class boundaries
| Aspect | Spectral Mixture Analysis (SMA) | Fuzzy Classification |
|---|---|---|
| Type | Physical model | Mathematical / logical model |
| Output | Fractions of materials (percentages) | Membership values (0–1) |
| Input | Spectral endmembers | Membership functions |
| Basis | Spectral reflectance mixing | Uncertainty & ambiguity of classes |
| Use case | Urban, vegetation density, soils, geology | Land-cover gradients, complex mosaics |
| Meaning | "How much of each material is inside the pixel?" | "To what degree does the pixel belong to each class?" |
Spectral Mixture Analysis (SMA)
Spectral Mixture Analysis is a soft classification technique that models each mixed pixel as a linear combination of pure endmember spectra. It estimates the fractional abundance of each land-cover component within the pixel.
Fuzzy Classification
Fuzzy classification assigns each pixel a degree of membership to multiple classes using fuzzy logic. It handles uncertainty and mixed pixels by allowing partial class belonging rather than forcing a single class label.
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