Skip to main content

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.


Comments

Post a Comment

Popular posts from this blog

Platforms in Remote Sensing

In remote sensing, a platform is the physical structure or vehicle that carries a sensor (camera, scanner, radar, etc.) to observe and collect information about the Earth's surface. Platforms are classified mainly by their altitude and mobility : Ground-Based Platforms Definition : Sensors mounted on the Earth's surface or very close to it. Examples : Tripods, towers, ground vehicles, handheld instruments. Applications : Calibration and validation of satellite data Detailed local studies (e.g., soil properties, vegetation health, air quality) Strength : High spatial detail but limited coverage. Airborne Platforms Definition : Sensors carried by aircraft, balloons, or drones (UAVs). Altitude : A few hundred meters to ~20 km. Examples : Airplanes with multispectral scanners UAVs with high-resolution cameras or LiDAR High-altitude balloons (stratospheric platforms) Applications : Local-to-regional mapping ...
Post a Comment
Read more

Types of Remote Sensing

Remote Sensing means collecting information about the Earth's surface without touching it , usually using satellites, aircraft, or drones . There are different types of remote sensing based on the energy source and the wavelength region used. 🛰️ 1. Active Remote Sensing 📘 Concept: In active remote sensing , the sensor sends out its own energy (like a signal or pulse) to the Earth's surface. The sensor then records the reflected or backscattered energy that comes back from the surface. ⚙️ Key Terminology: Transmitter: sends energy (like a radar pulse or laser beam). Receiver: detects the energy that bounces back. Backscatter: energy that is reflected back to the sensor. 📊 Examples of Active Sensors: RADAR (Radio Detection and Ranging): Uses microwave signals to detect surface roughness, soil moisture, or ocean waves. LiDAR (Light Detection and Ranging): Uses laser light (near-infrared) to measure elevation, vegetation...
Post a Comment
Read more

Resolution of Sensors in Remote Sensing

Spatial Resolution 🗺️ Definition : The smallest size of an object on the ground that a sensor can detect. Measured as : The size of a pixel on the ground (in meters). Example : Landsat → 30 m (each pixel = 30 × 30 m on Earth). WorldView-3 → 0.31 m (very detailed, you can see cars). Fact : Higher spatial resolution = finer details, but smaller coverage. Spectral Resolution 🌈 Definition : The ability of a sensor to capture information in different parts (bands) of the electromagnetic spectrum . Measured as : The number and width of spectral bands. Types : Panchromatic (1 broad band, e.g., black & white image). Multispectral (several broad bands, e.g., Landsat with 7–13 bands). Hyperspectral (hundreds of very narrow bands, e.g., AVIRIS). Fact : Higher spectral resolution = better identification of materials (e.g., minerals, vegetation types). Radiometric Resolution 📊 Definition : The ability of a sensor to ...
Post a Comment
Read more

geostationary and sun-synchronous

Orbital characteristics of Remote sensing satellite geostationary and sun-synchronous  Orbits in Remote Sensing Orbit = the path a satellite follows around the Earth. The orbit determines what part of Earth the satellite can see , how often it revisits , and what applications it is good for . Remote sensing satellites mainly use two standard orbits : Geostationary Orbit (GEO) Sun-Synchronous Orbit (SSO)  Geostationary Satellites (GEO) Characteristics Altitude : ~35,786 km above the equator. Period : 24 hours → same as Earth's rotation. Orbit type : Circular, directly above the equator . Appears "stationary" over one fixed point on Earth. Concepts & Terminologies Geosynchronous = orbit period matches Earth's rotation (24h). Geostationary = special type of geosynchronous orbit directly above equator → looks fixed. Continuous coverage : Can monitor the same area all the time. Applications Weather...
Post a Comment
Read more

Man-Made Disasters

  A man-made disaster (also called a technological disaster or anthropogenic disaster ) is a catastrophic event caused directly or indirectly by human actions , rather than natural processes. These disasters arise due to negligence, error, industrial activity, conflict, or misuse of technology , and often result in loss of life, property damage, and environmental degradation . Terminology: Anthropogenic = originating from human activity. Technological hazard = hazard caused by failure or misuse of technology or industry. 🔹 Conceptual Understanding Man-made disasters are part of the Disaster Management Cycle , which includes: Prevention – avoiding unsafe practices. Mitigation – reducing disaster impact (e.g., safety regulations). Preparedness – training and planning. Response – emergency actions after the disaster. Recovery – long-term rebuilding and policy correction. These disasters are predictable and preventable through strong...
Post a Comment
Read more