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