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Classification of Mixed Pixels



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:

  • spatial resolution is coarse (e.g., 30 m, 250 m)

  • land cover is patchy or complex

  • 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:

  1. Spectral Mixture Analysis (SMA)

  2. 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:

  • field spectrometry

  • image-derived signatures

  • spectral libraries (USGS)

Common endmembers:

  • vegetation

  • soil

  • water

  • impervious surface

✔ What SMA does

Instead of assigning a pixel to a single class, SMA tells you:

  • 40% vegetation

  • 30% soil

  • 30% built-up
    This is much more realistic for mixed pixels.

✔ Applications

  • Urban mapping (impervious surface fraction)

  • Forest canopy density

  • Burn severity

  • Soil–vegetation mixing analysis

  • Water content in wetlands

✔ Key Terminology

  • Endmember

  • Linear mixture model (LMM)

  • Fractional abundance

  • Spectral unmixing

  • Residual error (RMS error)

  • 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:

  • Forest = 0.6

  • Shrubland = 0.3

  • 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:

  • Membership functions (Gaussian, linear, sigmoidal)

  • Similarity measures

  • Fuzzy sets

  • Fuzzy rules (IF–THEN rules)

The output is a fuzzy membership map, not a single-class map.

✔ Advantages

  • Represents uncertainty

  • Better accuracy for heterogeneous landscapes

  • Natural for ecological and land-cover gradients

  • Useful for coarse resolution imagery (MODIS, AVHRR)

✔ Applications

  • Land-cover classification

  • Forest–nonforest transition zones

  • Wetland mapping

  • Urban–rural gradients

  • Agricultural mosaics

✔ Key Terminology

  • Fuzzy set

  • Membership function

  • Degree of belonging

  • Soft classification

  • Ambiguous class boundaries


AspectSpectral Mixture Analysis (SMA)Fuzzy Classification
TypePhysical modelMathematical / logical model
OutputFractions of materials (percentages)Membership values (0–1)
InputSpectral endmembersMembership functions
BasisSpectral reflectance mixingUncertainty & ambiguity of classes
Use caseUrban, vegetation density, soils, geologyLand-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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