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Accuracy Assessment


Accuracy assessment is the process of checking how correct your classified satellite image is.

👉 After supervised classification, the satellite image is divided into classes like:

  • Water

  • Forest

  • Agriculture

  • Built-up land

  • Barren land

But classification is done using computer algorithms, so some areas may be wrongly classified.

👉 Accuracy assessment helps to answer this question:

✔ "How much of my classified map is correct compared to real ground conditions?"

 Goal

The main goal is to:

  • Measure reliability of classified maps

  • Identify classification errors

  • Improve classification results

  • Provide scientific validity to research

👉 Without accuracy assessment, a classified map is not considered scientifically reliable.

Reference Data (Ground Truth Data)

Reference data is real-world information used to check classification accuracy.

It can be collected from:

✔ Field survey using GPS
✔ High-resolution satellite images (Google Earth etc.)
✔ Existing maps or survey reports

🧭 Example

Suppose your classified map shows:

  • A location classified as forest

But ground survey shows:

  • The same location is actually agriculture land

👉 This is a classification error.

📊 Error Matrix (Confusion Matrix)

What is Error Matrix?

Error matrix is a table used to compare classified results with actual ground data.

It is the most important tool in accuracy assessment.

🧾 Example of Error Matrix

Reference DataForestAgricultureWaterTotal
Forest405045
Agriculture630238
Water031417
Total463816100

👉 Diagonal values show correct classification
👉 Other values show classification errors

📚 Important Terminologies

1️⃣ Overall Accuracy

It shows how many pixels are correctly classified in total.


✔ Example

Correct pixels = 40 + 30 + 14 = 84
Total pixels = 100

👉 Overall Accuracy = 84%

2️⃣ Producer's Accuracy

✔ Meaning

Shows how well real-world features are correctly classified.

👉 It measures error of omission.

❓ What is Omission Error?

When a real feature is missed in classification.

Example:

  • Real forest area classified as agriculture.


3️⃣ User's Accuracy

✔ Meaning

Shows the probability that a classified pixel actually represents that class on ground.

👉 It measures error of commission.

❓ What is Commission Error?

When a pixel is wrongly included in a class.

Example:

  • Agriculture land classified as forest.

 Steps in Accuracy Assessment

Step 1 – Sampling Design

Selecting sample points to check accuracy.

Methods include:

✔ Random sampling
✔ Stratified sampling
✔ Systematic sampling

Step 2 – Comparison

Compare:

  • Classified image results

  • Ground truth data

Step 3 – Accuracy Calculation

Create error matrix and calculate:

  • Overall Accuracy

  • Producer's Accuracy

  • User's Accuracy

  • Kappa coefficient


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