Skip to main content

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.

đź“Š 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

đź“š 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


Comments

Post a Comment

Popular posts from this blog

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...
Post a Comment
Read more

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...
Post a Comment
Read more

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du cholĂ©ra dans Paris et le dĂ©partement de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...
Post a Comment
Read more

Representation of Spatial and Temporal Relationships

In GIS, spatial and temporal relationships allow the integration of location (the "where") and time (the "when") to analyze phenomena across space and time. This combination is fundamental to studying dynamic processes such as urban growth, land-use changes, or natural disasters. Key Concepts and Terminologies Geographic Coordinates : Define the position of features on Earth using latitude, longitude, or other coordinate systems. Example: A building's location can be represented as (11.6994° N, 76.0773° E). Timestamp : Represents the temporal aspect of data, such as the date or time a phenomenon was observed. Example: A landslide occurrence recorded on 30/07/2024 . Spatial and Temporal Relationships : Describes how features relate in space and time. These relationships can be: Spatial : Topological (e.g., "intersects"), directional (e.g., "north of"), or proximity-based (e.g., "near"). Temporal : Sequential (e....
Post a Comment
Read more

GIS: Real World and Representations - Modeling and Maps

Geographic Information Systems (GIS) serve as a bridge between the real world and digital representations of geographic phenomena. These representations allow users to store, analyze, and visualize spatial data for informed decision-making. Two key aspects of GIS in this context are modeling and maps , both of which are used to represent real-world geographic features and phenomena in a structured, analyzable format. Let's delve into these concepts, terminologies, and examples in detail. 1. Real World and Representations in GIS Concept: The real world comprises physical, tangible phenomena, such as landforms, rivers, cities, and infrastructure, as well as more abstract elements like weather patterns, population densities, and traffic flow. GIS allows us to represent these real-world phenomena digitally, enabling spatial analysis, decision-making, and visualization. The representation of the real world in GIS is achieved through various models and maps , which simplify...
Post a Comment
Read more