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

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Energy Interaction with Atmosphere and Earth Surface

In Remote Sensing , satellites record electromagnetic radiation (EMR) that is reflected or emitted from the Earth. Before reaching the sensor, radiation interacts with: The Atmosphere The Earth's Surface These interactions control how satellite images look and how we interpret them. I. Interaction of EMR with the Atmosphere When solar radiation travels from the Sun to the Earth, four main processes occur: 1. Absorption Definition: Absorption occurs when atmospheric gases absorb radiation at specific wavelengths and convert it into heat. Main absorbing gases: Ozone (O₃) → absorbs Ultraviolet (UV) Carbon dioxide (CO₂) → absorbs Thermal Infrared Water vapour (H₂O) → absorbs Infrared Concept: Atmospheric Windows These are wavelength regions where absorption is very low, allowing radiation to pass through the atmosphere. Remote sensing depends on these windows. For example, satellites like Landsat 8 use visible, near-infrared, and thermal bands located in atmospheric windows. 2. Trans...

Landsat band composition

Short-Wave Infrared (7, 6 4) The short-wave infrared band combination uses SWIR-2 (7), SWIR-1 (6), and red (4). This composite displays vegetation in shades of green. While darker shades of green indicate denser vegetation, sparse vegetation has lighter shades. Urban areas are blue and soils have various shades of brown. Agriculture (6, 5, 2) This band combination uses SWIR-1 (6), near-infrared (5), and blue (2). It's commonly used for crop monitoring because of the use of short-wave and near-infrared. Healthy vegetation appears dark green. But bare earth has a magenta hue. Geology (7, 6, 2) The geology band combination uses SWIR-2 (7), SWIR-1 (6), and blue (2). This band combination is particularly useful for identifying geological formations, lithology features, and faults. Bathymetric (4, 3, 1) The bathymetric band combination (4,3,1) uses the red (4), green (3), and coastal bands to peak into water. The coastal band is useful in coastal, bathymetric, and aerosol studies because...

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...

Landsat 8 Band designation and Band Combination.

Landsat 8 Band designation and Band Combination.  Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands Wavelength (micrometers) Resolution (meters) Band 1 - Coastal aerosol 0.43-0.45 30 Band 2 - Blue 0.45-0.51 30 Band 3 - Green 0.53-0.59 30 Band 4 - Red 0.64-0.67 30 Band 5 - Near Infrared (NIR) 0.85-0.88 30 Band 6 - SWIR 1 1.57-1.65 30 Band 7 - SWIR 2 2.11-2.29 30 Band 8 - Panchromatic 0.50-0.68 15 Band 9 - Cirrus 1.36-1.38 30 Band 10 - Thermal Infrared (TIRS) 1 10.6-11.19 100 Band 11 - Thermal Infrared (TIRS) 2 11.50-12.51 100 Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://www.facebook.com/Applied.Geography http://geogisgeo.blogspot.com