Skip to main content

Digital Image

Electromagnetic energy may be detected either photographically or electronically. The photographic process uses chemical reactions on the surface of light-sensitive film to detect and record energy variations. It is important to distinguish between the terms images and photographs in remote sensing. An image refers to any pictorial representation, regardless of what wavelengths or remote sensing device has been used to detect and record the electromagnetic energy. A photograph refers specifically to images that have been detected as well as recorded on photographic film. The black and white photo to the left, of part of the city of Ottawa, Canada was taken in the visible part of the spectrum. Photos are normally recorded over the wavelength range from 0.3 µm to 0.9 µm - the visible and reflected infrared. Based on these definitions, we can say that all photographs are images, but not all images are photographs. Therefore, unless we are talking specifically about an image recorded photographically, we use the term image.


A photograph could also be represented and displayed in a digital format by subdividing the image into small equal-sized and shaped areas, called picture elements or pixels, and representing the brightness of each area with a numeric value or digital number. Indeed, that is exactly what has been done to the photo to the left. In fact, using the definitions we have just discussed, this is actually a digital image of the original photograph! The photograph was scanned and subdivided into pixels with each pixel assigned a digital number representing its relative brightness. The computer displays each digital value as different brightness levels. Sensors that record electromagnetic energy, electronically record the energy as an array of numbers in digital format right from the start. These two different ways of representing and displaying remote sensing data, either pictorially or digitally, are interchangeable as they convey the same information (although some detail may be lost when converting back and forth).


In previous sections we described the visible portion of the spectrum and the concept of colours. We see colour because our eyes detect the entire visible range of wavelengths and our brains process the information into separate colours. Can you imagine what the world would look like if we could only see very narrow ranges of wavelengths or colours? That is how many sensors work. The information from a narrow wavelength range is gathered and stored in a channel, also sometimes referred to as a band. We can combine and display channels of information digitally using the three primary colours (blue, green, and red). The data from each channel is represented as one of the primary colours and, depending on the relative brightness (i.e. the digital value) of each pixel in each channel, the primary colours combine in different proportions to represent different colours.



When we use this method to display a single channel or range of wavelengths, we are actually displaying that channel through all three primary colours. Because the brightness level of each pixel is the same for each primary colour, they combine to form a black and white image, showing various shades of gray from black to white. When we display more than one channel each as a different primary colour, then the brightness levels may be different for each channel/primary colour combination and they will combine to form a colour image.


Comments

Popular posts from this blog

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...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

Scope of Disaster Management

Disaster management refers to the systematic approach to managing and mitigating the impacts of disasters, encompassing both natural hazards (e.g., earthquakes, floods, hurricanes) and man-made disasters (e.g., industrial accidents, terrorism, nuclear accidents). Its primary objectives are to minimize potential losses, provide timely assistance to those affected, and facilitate swift and effective recovery. The scope of disaster management is multifaceted, encompassing a series of interconnected activities: preparedness, response, recovery, and mitigation. These activities must be strategically implemented before, during, and after a disaster. Key Concepts, Terminologies, and Examples 1. Awareness: Concept: Fostering public understanding of potential hazards and appropriate responses before, during, and after disasters. This involves disseminating information about risks, safety measures, and recommended actions. Terminologies: Hazard Awareness: Recognizing the types of natural...

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...

Role of Geography in Disaster Management

Geography plays a pivotal role in disaster management by facilitating an understanding of the impact of natural disasters, guiding preparedness efforts, and supporting effective response and recovery. By analyzing geographical features, environmental conditions, and historical data, geography empowers disaster management professionals to identify risks, plan for hazards, respond to emergencies, assess damage, and monitor recovery. Geographic Information Systems (GIS) serve as crucial tools, providing critical spatial data for informed decision-making throughout the disaster management cycle. Key Concepts, Terminologies, and Examples 1. Identifying Risk: Concept: Risk identification involves analyzing geographical areas to understand their susceptibility to specific natural disasters. By studying historical events, topography, climate patterns, and environmental factors, disaster management experts can predict which regions are most vulnerable. Terminologies: Hazard Risk: The pr...