Application Of Signal Processing In Satellite Imaging Computer Science Essay

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Abstract: This term paper is basically based on the application of signal processing that is used in satellite imaging. Satellite imaging can be done in different ways like phase congruency model, by bi-orthogonal wavelets, by affine projection etc. But one thing is common in all of them is they all are using signal processing. Satellite imaging is basically gathering of information from sensors mounted on orbiting satellites to capture features on earth's surface. However satellite images are of three types including visible imaging, infrared imaging and water vapor imaging. All of these are discussed in detail in later sections.

Keywords: SAR, ANN, satellite imagery,filters.


Satellite imaging refers to gathering information from sensors mounted on orbiting satellites to capture features on the Earth's surface. There are two types of sensor systems known as 'active' and 'passive'.

An 'active' refers to propagate its own electro-magnetic radiation, and measures (as digital numbers), the intensity of the return signal. Synthetic Aperture Radar (SAR) is an example of an active system.

A 'passive' refers to generally consists of an array of small sensors or detectors, which record (as digital numbers), the amount of electro-magnetic radiation reflected and/or emitted from the Earth's surface. A multi-spectral scanner is an example of a passive system.

Fig 1. Remote sensing satellite

The digital data acquired by satellites is transmitted to ground stations and can be used to reconstitute an image of the Earth's surface. When processed, satellite images may resemble an aerial photograph taken from a very high altitude, but in fact there are some very important differences which enable much more information to be determined about the areas of interest.

These differences include:

acquisition of infra-red reflectances enable better identification and assessment of features

images are acquired on a regular basis enabling access to current and archived data

images can be produced in photographic or digital form

cheap and user friendly software is now available to view and analyse digital imagery on PCs[1]


Satellite imaging consist of photographs of earth or other planets made by means of artificial satellites. Data acquired by Earth observation satellites provide a number of benefits for studying the Earth's surface, including:

ability to combine satellite digital data with other digital data

continuous acquisition of data

good spatial resolution

regular revisit capabilities (resulting in up-to-date information)

broad regional coverage

good spectral resolution (including infra-red bands)

ability to manipulate/enhance digital data

cost effective data

map-accurate data

possibility of stereo viewing

large archive of historical data[1]


Satellites have been used over the past several decades to obtain a wide variety of information about the earth's surface, ranging from military applications to tracking global weather patterns, tectonic activity, surface vegetation, ocean currents and temperatures, polar ice fluctuations, pollution, and many other aspects. The application of satellite image analysis to archaeology has emerged alongside these other uses, but archaeologists are only now beginning to exploit more fully the broad range of analytical tools available for assessing the satellite image data of the earth's surface and sub-surface. The decreasing costs (e.g., as low as $10), the increasing image resolutions (e.g., under one metre), and the greater availability (e.g., on-line purchasing) of satellite images for the general public is now making it possible for archaeologists to use satellite images more fully. The evolution of satellite image technology is also enabling the manipulation of a greater range of data contained in increasing types of satellite images (e.g., Aster; Corona; Landsat TM, etc.): archaeologists can now examine a broad spectrum of reflectivity signatures and bands within and between archaeological sites, including both surface and sub-surface features.



Algorithm to get images of satellite using matlab

Step 1: Read the original image and display it.

Fig2. original image

Step 2: Convert image from RGB color space to YCBCR color space.

Fig 3. Image after coloring

Step 3: Above image is given as an input for both the classifiers i.e. K-Means classifiers and

Neural Network Classifiers. Hereafter we have compared the results obtained from both the classifiers.

Step 4: Label every pixel in the image using the results from K-Means. For every object in

our input, K-means returns an index corresponding to a cluster. Label every pixel in

the image with its cluster-index. Output obtained under K-Means Classifier is

shown in Fig.3

Fig 4. Output from K-Means Classifier

Fig 5. Output from ANN classifier.

Step 5:. Use above images to separate out three different color objects. [4, 5, 15].

Objects in cluster 1


Fig. 6(a) Objects in cluster 1 by K-Means and

ANN classifier.

Step 6: Objects in Cluster 2

Fig. 7(a) objects in cluster 2 by K-Means and

ANN classifier.

Step 7: Objects in Cluster 3

Fig. 8(a) Objects in cluster 3 by K-Means and ANN classifier

Step 8:Graphical user interface So as to make this project user friendly, graphical user interfaces are given.[5]

Fig 9. Final image in matlab


There are three main types of satellite images available:

VISIBLE IMAGERY: Visible satellite pictures can only be viewed during the day, since clouds reflect the light from the sun. On these images, clouds show up as white, the ground is normally grey, and water is dark. In winter, snow-covered ground will be white, which can make distinguishing clouds more difficult. To help differentiate between clouds and snow, looping pictures can be helpful; clouds will move while the snow won't. Snow-covered ground can also be identified by looking for terrain features, such as rivers or lakes. Rivers will remain dark in the imagery as long as they are not frozen. If the rivers are not visible, they are probably covered with clouds. Visible imagery is also very useful for seeing thunderstorm clouds building. Satellite will see the developing thunderstorms in their earliest stages, before they are detected on radar.

FIG 10 Visible image of satellite

INFRARED IMAGERY: Infrared satellite pictures show clouds in both day and night. Instead of using sunlight to reflect off of clouds, the clouds are identified by satellite sensors that measure heat radiating off of them. The sensors also measure heat radiating off the surface of the earth. Clouds will be colder than land and water, so they are easily identified. Infrared imagery is useful for determining thunderstorm intensity. Strong to severe thunderstorms will normally have very cold tops. Infrared imagery can also be used for identifying fog and low clouds. The fog product combines two different infrared channels to see fog and low clouds at night, which show up as dark areas on the imagery.

Fig 11. Infrared image of satellite

WATER VAPOR IMAGERY: Water vapor satellite pictures indicate how much moisture is present in the upper atmosphere (approximately from 15,000 ft to 30,000 ft). The highest humidities will be the whitest areas while dry regions will be dark. Water vapor imagery is useful for indicating where heavy rain is possible. Thunderstorms can also erupt under the high moisture plumes. [2]

Fig 12 Water vapor image of satellite





Digital signal processing has basically revolutionized the telecommunications industry. It is used in many telecommunication systems today; for instance, in telephone systems for dual-tone multi-frequency (DTMF) signaling, echo canceling of telephone lines and equalizers used in high-speed telephone modems. Further, error-correcting codes are used to protect digital signals from bit errors during transmission (or storing) and different data compression algorithms are utilized to reduce the number of data bits needed to represent a given amount of information. Digital signal processing is also used in many contexts in cellular telephone systems, for instance speech coding in mobile or global systems for mobile communication (GSM) telephones, modulators and demodulators, voice scrambling and other cryptographic devices. It is very common to find five to ten microcontrollers in a low-cost cellular telephone. An application dealing with high frequency is the directive antenna having an electronically controlled beam. By using directive antennas at the base stations in a cellular system, the base station can " point " at the mobile at all times, thereby reducing the transmitter (TX) power needed. This in turn increases the capacity of a fixed bandwidth system in terms of the number of simultaneous users per square mile, and so increases the service level and the revenue for the system operator. The increased use of the Internet implies the use of digital processing in many layers, not only for signal processing in asymmetric digital subscriber loop (ADSL) and digital subscriber loop (DSL) modems, but also for error correction, data compression (images and audio) and protocol handling.[3]


In most audio and video equipment today, such as DVD and CD players, digital audio tape (DAT), and MP3 players, digital signal processing is mandatory. This is also true for most modern studio equipment as well as more or less advanced synthesizers used in today's music production. Digital signal processing has also made many new noise suppression and companding systems (e.g., Dolbyâ„¢) attractive.

Digital methods are not only used for producing and storing audio and video information, but also for distribution. This could be between studios and transmitters, or even directly to the end user, such as in the digital audio broadcasting (DAB) system. Digital transmission is also used for broadcasting of television (TV) signals. High definition television (HDTV) systems utilize many digital image processing

techniques. Digital image processing can be regarded as a special branch of digital processing having many things in common with digital signal processing, but dealing mainly with two-dimensional image signals. Digital image processing can be used for many tasks, e.g., restoring distorted or blurred images, morphing, data compression by image coding, identification and analysis of pictures and photos.[3]

Biomedical Applications

DSP is used extensively in the field of biomedicine. In it, the various signals that are generated by the different organs in the human body are measured in order to find information regarding the health of the same. For example, in case of electrocardiograms, the electric signals generated by the heart are measured. Similarly, the activity of the brain is monitored by electroencephalograms.[4]


One major problem in long-distance phone communication is echo due to the time delays. DSP helps to solve this problem by measuring the returned signal and then creating an antisignal that cancels the echo. This technique is also used in speakerphones to get rid of audio feedback.

DSP has caused a revolution in radar systems. It has allowed compressing of the RF pulse after it is received, filtering to reduce noise, and selecting and generating various pulse widths and shapes-all at speeds of several hundred megahertz! All of this has increased the range of radar and given better distance determination.

As in many other areas, DSP has solved major problems with medical equipment. A computed tomography (CT) scanner uses signals from many x-rays and stores these as digital data. Using DSP techniques, this data is used to calculate images that represent slices through the human body, which show a lot more detail than earlier techniques and allow better diagnosing and treatment. In 1979, Godfrey N. Hounsfield and Allan M. Cormack shared the Nobel Prize in Medicine for their work on CT. (Computed tomography was originally called computed axial tomography, or CAT scanning. This

term is still often used by the public, but is frowned upon by medical professionals.)[3]



I Sakshi Sachdeva, hereby express my deep gratitude to all those who helped me in the accomplishment of my dsp term paper. First of all, I would like to express my thanks to GOD almighty, whose grace and bliss gave me the strength and confidence to accomplish this project.

Then I want to acknowledges all the contributors involved in the preparation of this term paper. Including me, there is a hand of my teachers, some books and internet. I express most gratitude to my subject teacher, Mr. Govardan Rao Talluri who guided me in the right direction. The guidelines provided by him helped me a lot to cope up with the complexities of SATELLITE IMAGING and completing the term paper.

The books and websites I consulted helped me to describe each and every point mentioned in this project. Help of original creativity and illustration had taken and I have explained each and every aspect of the project precisely.

Last but not least I acknowledges all the members who are involved in the preparation of this term paper.