Skip to main content

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

Popular posts from this blog

geostationary and sun-synchronous

Orbital characteristics of Remote sensing satellite geostationary and sun-synchronous  Orbits in Remote Sensing Orbit = the path a satellite follows around the Earth. The orbit determines what part of Earth the satellite can see , how often it revisits , and what applications it is good for . Remote sensing satellites mainly use two standard orbits : Geostationary Orbit (GEO) Sun-Synchronous Orbit (SSO)  Geostationary Satellites (GEO) Characteristics Altitude : ~35,786 km above the equator. Period : 24 hours → same as Earth's rotation. Orbit type : Circular, directly above the equator . Appears "stationary" over one fixed point on Earth. Concepts & Terminologies Geosynchronous = orbit period matches Earth's rotation (24h). Geostationary = special type of geosynchronous orbit directly above equator → looks fixed. Continuous coverage : Can monitor the same area all the time. Applications Weather...

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

Disaster Risk

Disaster Risk 

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...

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...