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.

Comments

Kuhn’s Paradigms

The given content explains Thomas S. Kuhn’s model of scientific development , its application to geography, and criticisms by Karl Popper, Paul Feyerabend, Michel Foucault , and others. 1. Basic Idea Kuhn argued that science does not develop continuously in a straight line . Instead, scientific development occurs through: Preparadigm → Paradigm → Normal Science → Crisis → Scientific Revolution → New Paradigm A new paradigm may replace an older one, producing a major change in the way scientists understand and study a subject. Concepts and Terminologies Concept / Term Simple Meaning Paradigm A commonly accepted framework/model that guides scientific research Exemplar A successful concrete problem-solution used as a model for future research Disciplinary Matrix Shared beliefs, values, concepts, methods and techniques of a scientific community Preparadig...

Regional Geography, Systematic Geography, Idiographic, Nomothetic, Inductive and Deductive Approaches

T wo major ways of studying Geography : the Regional Approach and the Systematic Approach . It also explains the related ideas of idiographic vs. nomothetic and inductive vs. deductive reasoning , especially in the context of the Hartshorne–Schaefer debate . 1. Regional Geography: “All About One” Regional Geography studies one particular region in detail . A region is an area that has some degree of homogeneity (sameness) within its boundary but is also unique or different from other regions . For example, if we study Palakkad District , we may study: Relief and drainage Climate Soil Vegetation Agriculture Population Occupation Economy Culture Political characteristics The purpose is to understand the complete geographical personality of Palakkad and the relationships among its different features. Key concepts Region: A geographical area with identifiable characteristics and boundaries. Homogeneity: Si...

Models and Theories in Geography

Geographical Models A model is a simplified representation of reality used to describe, explain, simulate, and predict geographical phenomena. Types Physical (Iconic) Models – Three-dimensional representations (e.g., globe, relief model). Conceptual Models – Diagrams or frameworks explaining geographical relationships. Mathematical (Quantitative) Models – Equations and statistical models for spatial analysis and prediction. Simulation Models – Computer-based models that simulate geographical processes (e.g., climate, flood, urban growth). Major Geographical Models Model Scholar Year Concept Johann Heinrich von Thünen Agricultural Land Use Model 1826 Land use varies with distance from the market. Walter Christaller Central Place Model 1933 Distribution of settlements and services. Ernest Burgess Concentric Zone M...

History of Geography.

Chronological sequence and categorized by era and region. I. Introduction & Etymology • Etymology : The term "Geography" derives from the Greek γεωγραφία (geographia) , meaning "Earth-writing" (description or writing about the Earth). • First Use : Eratosthenes (276–194 BC) was the first person to use the word. • Pre-Term Practices : Recognizable geographic practices like cartography (map-making) existed prior to the coining of the term. II. Antiquity & Ancient Civilizations 1. Ancient Egypt (Pre-Classical) • Cosmology : Ancient Egyptians viewed the Nile as the center of the world, with existence based upon "the" river. • Geographical Knowledge : • Oases : Known to the east and west, considered locations of various gods (e.g., Siwa for the god Amon ). • Kushitic Region : Lay to the south, known as far as the 4th cataract . • Punt : A region located south a...

Multispectral and Hyperspectral Imaging Systems

The main idea is how a remote-sensing sensor collects information about an area . A sensor does not simply take an ordinary photograph. It measures the electromagnetic energy reflected or emitted by objects in different wavelength bands . Depending on how many bands are measured and how the sensor collects them, different imaging systems are used. 1. Multispectral vs. Hyperspectral Multispectral imaging (MSI) records information in a limited number of relatively broad, separate spectral bands , such as blue, green, red, near-infrared and shortwave infrared. Hyperspectral imaging (HSI) records information in many narrow and usually contiguous spectral bands . Therefore, it provides a much more detailed spectral signature of each pixel. The resulting dataset is commonly called a hyperspectral data cube (hypercube) because it contains: X-axis → spatial information Y-axis → spatial information Z-axis → wavelength/spectral information Thus, hype...

Vineesh V, Geography

Search This Blog