-
Location – Where the object is found on the map or photo. Knowing the place can give clues about what it is.
-
Size – How big or small it appears, which helps identify objects (e.g., a football field vs. a garden).
-
Shape – The outline or form of the object, such as round, rectangular, or irregular.
-
Shadow – The dark area an object casts; it helps guess height, shape, and type of object.
-
Tone/Color – Lightness, darkness, or color differences that help tell objects apart (e.g., blue water, green vegetation).
-
Texture – How smooth or rough the surface looks in the image (e.g., forest appears rough, grassland appears smooth).
-
Pattern – The arrangement or repetition of objects, like rows of trees or grid-like city blocks.
-
Height/Depth – How tall or deep an object or landform is, often estimated from shadows or stereo images.
-
Site/Situation/Association – The surroundings and relationships between objects (e.g., a swimming pool next to a house, or a factory near a railway line).
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 🧠Exampl...
Comments
Post a Comment