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

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Vineesh V, Geography

Search This Blog