Satellite Image Processing is an important field in research and development and consists of the images of earth and satellites taken by the means of artificial satellites. Firstly, the photographs are taken in digital form and later are processed by the computers to extract the information. Statistical methods are applied to the digital images and after processing the various discrete surfaces are identified by analyzing the pixel values.
The satellite imagery is widely used to plan the infrastructures or to monitor the environmental conditions or to detect the responses of upcoming disasters.
In broader terms we can say that the Satellite Image Processing is a kind of remote sensing which works on pixel resolutions to collect coherent information about the earth surface.
Majorly there are four kinds of resolutions associated with satellite imagery. These are:
- Spatial resolution –
It is determined by the sensors Instantaneous Field of View(IFoV) and is defined as the pixel size of an image that is visible to the human eye being measured on the ground. Since it has high resolving power or the ability to separate and hence is termed as Spatial Resolution.
- Spectral resolution –
This resolution measures the wavelength internal size and determines the number of wavelength intervals that the sensor measures.
- Temporal resolution –
The word temporal is associated with time or days and is defined as the time that passes between various imagery cloud periods.
- Radiometric resolution –
This resolution provides the actual characteristics of the image and is generally expressed in bits size. It gives the effective bit depth and records the various levels of brightness of imaging system.
Thus, Satellite Image Processing has huge amount of applications in research and development fields, in remote sensing, in astronomy and now even in cloud computing on a large scale.