Supervised classification in remote sensing

Supervised classification in remote sensing is a method of using labeled training data to automatically classify pixels or areas in an image. The process typically involves the following steps:

Collecting and preprocessing the image data: This includes acquiring the image data from a remote sensing platform such as a satellite or aircraft, and performing any necessary preprocessing steps such as atmospheric correction or geometric rectification.

Defining training areas and collecting training data: This involves manually identifying and labeling different classes of land cover within the image, such as forests, urban areas, or water bodies. These labeled training areas are used to train the classification algorithm.

Training the classification algorithm: A supervised classification algorithm such as maximum likelihood, decision tree, or support vector machine is used to learn the relationship between the image data and the labeled training data.

Applying the trained classification algorithm to the image: The trained algorithm is then applied to the entire image to classify each pixel or area into one of the defined classes.

Post-classification processing and accuracy assessment: The final classified image is then visually inspected and evaluated for accuracy using metrics such as overall accuracy, kappa coefficient, and confusion matrix.

For illustration, imagine a satellite image of a city. The image is preprocessed to correct for atmospheric effects and geometric distortions. The analyst then defines several training areas within the image, such as a residential area, a commercial area, and a park. They label each of these areas with their corresponding land-use class, such as residential, commercial, and park. The classification algorithm is then trained using these labeled training areas. Once the algorithm is trained, it is applied to the entire image, resulting in a classified image where each pixel is assigned a class label, such as residential, commercial, or park.

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Optical Sensors in Remote Sensing

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Platforms in Remote Sensing

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Radar Sensors in Remote Sensing

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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...

Thermal Sensors in Remote Sensing

Thermal sensors are remote sensing instruments that detect naturally emitted thermal infrared (TIR) radiation from the Earth's surface. Unlike optical sensors (which detect reflected sunlight), thermal sensors measure heat energy emitted by objects because of their temperature. They work mainly in the Thermal Infrared region (8–14 µm) of the electromagnetic spectrum. 1. Thermal Infrared Radiation All objects above 0 Kelvin (absolute zero) emit electromagnetic radiation. This is explained by Planck's Radiation Law . For Earth's surface temperature range (about 250–330 K), the peak emitted radiation occurs in the 8–14 µm thermal window . Thus, thermal sensors detect emitted energy , not reflected sunlight. 2. Emissivity Emissivity is the efficiency with which a material emits thermal radiation. Values range from 0 to 1 : Water, vegetation → high emissivity (0.95–0.99) Bare soil → medium (0.85–0.95) Metals → low (0.1–0.3) E...

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