Skip to main content

Post classification smoothing in remote sensing

Post classification smoothing in remote sensing is a technique used to reduce the amount of noise and improve the overall accuracy of a classification. It is typically applied after the initial classification has been completed and is used to smooth out any inconsistencies or errors that may be present in the classified image.


The main goal of post classification smoothing is to improve the overall accuracy of the classification by reducing the amount of noise and smoothing out any inconsistencies or errors that may be present in the classified image. This is typically achieved by applying mathematical algorithms that take into account the spatial relationships between pixels in the image, as well as the spectral characteristics of the pixels.


Post-classification smoothing in remote sensing refers to a technique used to reduce the "salt and pepper" noise that can occur in a classified image. This noise can be caused by errors in the classification process or by variations in the underlying data. Post-classification smoothing applies a filter to the classified image that smooths out small clusters of pixels that belong to a different class than their surrounding pixels. This can improve the overall visual appearance of the image and make it easier to interpret. Some common methods for post-classification smoothing include majority filter, median filter, and a filter based on Markov random fields.


There are several different techniques that can be used for post classification smoothing, including:


Majority filter: this technique uses the majority class of the surrounding pixels to determine the class of the current pixel.


Median filter: this technique uses the median class of the surrounding pixels to determine the class of the current pixel.


Mode filter: this technique uses the most common class of the surrounding pixels to determine the class of the current pixel.


Convolution filter: this technique uses a convolution kernel to apply a mathematical operation to the surrounding pixels, which is then used to determine the class of the current pixel.


Overall, post classification smoothing is an important step in the remote sensing classification process, as it helps to improve the overall accuracy of the classification and reduce the amount of noise and inconsistencies in the image.






Comments

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more

Discrete Detectors and Scanning mirrors Across the track scanner Whisk broom scanner.

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Across-Track Scanner or Whisk Broom Scanner) Multispectral Imaging:  This technique involves capturing images of the Earth's surface using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation.  This allows for the identification of various features and materials based on their spectral signatures. Discrete Detectors:  These are individual sensors that are arranged in a linear or array configuration.  Each detector is responsible for measuring the radiation within a specific wavelength band. Scanning Mirrors:  These are optical components that are used to deflect the incoming radiation onto the discrete detectors.  By moving the mirrors,  the sensor can scan across the scene,  capturing data from different points. Across-Track Scanner or Whisk Broom Scanner:  This refers to the scanning mechanism where the mirror moves perpendicular to the direction of flight.  This allows for t...
Post a Comment
Read more

Satalite

Landsat → Land resources SPOT → High-resolution mapping IRS → Indian natural-resource mapping ASTER → Geology + thermal + DEM QuickBird → Very high spatial resolution MODIS → Daily global monitoring GOES → Weather monitoring AVHRR → Weather + vegetation + ocean AVIRIS → Hyperspectral imaging Highest spectral resolution: AVIRIS (224 narrow bands) Highest spatial resolution in this list: QuickBird (~0.61 m PAN) Highest temporal frequency: GOES (minutes) Best broad global monitoring: MODIS Indian sensors: IRS-LISS III and LISS IV Hyperspectral: AVIRIS Thermal + multispectral + DEM: ASTER abbreviations MSS – Multispectral Scanner System TM – Thematic Mapper ETM+ – Enhanced Thematic Mapper Plus GOES – Geostationary Operational Environmental Satellite AVHRR – Advanced Very High Resolution Radiometer HRV – High Resolution Visible HRVIR – High Resolution Visible and Infrared HRG – High Resolution Geometric ...
Post a Comment
Read more