Grey level thresholding. Level slicing. Contrast stretching lo p

Grey level thresholding.

Level slicing.

Contrast stretching.

Image enhancement

Lillesand and Kiefer (1994) explained the goal of image enhancement procedures is to improve the visual interpretability of any image by increasing the apparent distinction between the features in the scene. This objective is to create "new" image from the original image in order to increase the amount of information that can be visually interpreted from the data.

Enhancement operations are normally applied to image data after the appropriate restoration procedures have been performed. Noise removal, in particular, is an important precursor to most enhancements. In this study, typical image enhancement techniques are as follows:

Grey level thresholding

Grey level thresholding is a simple lookup table, which partitions the gray levels in an image into one or two categories - those below a user-selected threshold and those above. Thresholding is one of many methods for creating a binary mask for an image. Such masks are used to restrict subsequent processing to a particular region within an image.

This procedure is used to segment an input image into two classes: one for those pixels having values below an analyst- defined gray level and one for those above this value. (Lillesand and Kiefer, 1994).

Level slicing

Level slicing is an enhancement technique whereby the Digital Numbers (DN) distributed along the x-axis of an image histogram is divided into a series of analyst-specified intervals of "slices". All of DNs falling within a given interval in the input image are then displayed at a single DN in the output image (Lillesand and Kiefer, 1994).

Contrast stretching

Most satellites and airborne sensor were designed to accommodate a wide range of illumination conditions, from poorly lit arctic regions to high reflectance desert regions. Because of this, the pixel values in the majority of digital scenes occupy a relatively small portion of the possible range of image values. If the pixel values are displayed in their original form, only a small range of gray values will be used, resulting in a low contrast display on which similar features night is indistinguishable.

A contrast stretch enhancement expands the range of pixel values so that they are displayed over a fuller range of gray values. (PCI, 1997)

Generally, image display and recording devices typically operate over a range of 256 gray levels (the maximum number represent in 8-bit computer encoding). In the case of 8-bit single image, is to expand the narrow range of brightness values typically present in an output image over a wider range of gray value. The result is an output image that is designed to accentuate the contrast between features of interest to the image analyst (Lillesand and Kiefer, 1994).

The grey level or grey value indicates the brightness of a pixel. The minimum grey level is 0. The maximum grey level depends on the digitisation depth of the image. For an 8-bit-deep image it is 255. In a binary image a pixel can only take on either the value 0 or the value 255.

Comments

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl

DRA Disaster Risk Assessment

Disaster Risk Assessment (DRA): A Professional Overview Disaster Risk Assessment (DRA) is a systematic process used to identify, analyze, and evaluate the potential hazards, vulnerabilities, and risks posed by disasters to people, property, infrastructure, and the environment. It is a critical tool for effective disaster risk management, enabling communities, organizations, and governments to make informed decisions and implement appropriate mitigation measures. Key Components of DRA Hazard Identification: Identifying the types of hazards that could potentially affect a specific area, such as natural disasters (earthquakes, floods, cyclones), technological disasters (industrial accidents, infrastructure failures), or man-made disasters (conflicts, pandemics). Vulnerability Assessment: Evaluating the susceptibility of people, infrastructure, and the environment to the identified hazards. This involves assessing factors such as location, construction quality, socio-economic co

Linear Arrays Along-Track Scanners or Pushbroom Scanners

Multispectral Imaging Using Linear Arrays (Along-Track Scanners or Pushbroom Scanners) Multispectral Imaging: As previously defined, this involves capturing images using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. Linear Array of Detectors (A): This refers to a row of discrete detectors arranged in a straight line. Each detector is responsible for measuring the radiation within a specific wavelength band. Focal Plane (B): This is the plane where the image is formed by the lens system. It is the location where the detectors are placed to capture the focused image. Formed by Lens Systems (C): The lens system is responsible for collecting and focusing the incoming radiation onto the focal plane. It acts like a camera lens, creating a sharp image of the scene. Ground Resolution Cell (D): As previously defined, this is the smallest area on the ground that can be resolved by a remote sensing sensor. In the case of linear array scanne

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

Hazard Vulnerability Exposure Risk

Key Concepts in Hazard Identification, Vulnerability Assessment, Exposure Assessment, and Risk Analysis Hazard-Exposure-Vulnerability-Risk (HEVR) Framework: Hazard: A potential event or phenomenon that can cause harm. Exposure: People, assets, or environments in harm's way. Vulnerability: Susceptibility to damage or harm from a hazard. Risk: The potential for loss or damage resulting from the interaction of hazards, exposure, and vulnerability. Risk as a Function: Risk can be calculated using the formula: Risk = Hazard × Vulnerability × Exposure. Reducing any of these factors can decrease overall risk. Types of Hazards: Natural hazards: Earthquakes, floods, tsunamis, landslides, hurricanes. Anthropogenic hazards: Industrial accidents, pollution, infrastructure failure, climate change. Technological hazards: Nuclear accidents, chemical spills. Vulnerability Dimensions: Physical: Infrastructure quality, building codes, location. Social: Age, income, disability, gender, acces

Vineesh V, Geography

Search This Blog