Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/6756
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dc.contributor.authorHamad, Nahid Hamid
dc.contributor.authorSupervisor - Naji Zumrawi
dc.date.accessioned2014-08-21T11:15:54Z
dc.date.available2014-08-21T11:15:54Z
dc.date.issued2010-01-01
dc.identifier.citationHamad,Nahid Hamid .Digital Image Processing in Remote Sensing/Nahid Hamid Hamad;Naji Zumrawi.-Khartoum:Sudan University of Science and Technology,College of Engineering,2010.- 79P. : ill. ; 28Cm.-M.Sc.en_US
dc.identifier.urihttp://repository.sustech.edu/handle/123456789/6756
dc.descriptionThesisen_US
dc.description.abstractThe importance of remote sensing in its various types aerial photos, satellite images, radar and others is that all the mentioned types help in the process of continuous monitoring of earth resources. They also provide more information about the earth, that the digital image acquired a great significance in surveying and for the production of topographic maps. Therefore it has become important to process the digital image by enhancing and improving the clarity and classifying its features so as to obtain the required information. Some tests and treatments using the ERDAS IMAGINE software were carried out to enhance and classify features on (digital images) the satellite image (Landsat 4) which cover Khartoum State at a resolution of 30m. This image was taken at the year 2000. The image did not need to be adjusted as it was coordinated to(x=431747, y=1742920) for upper left corner and (x=465405.5, y=1704986) for lower right corner. In this research it has been found that the Histogram equalization provides a better result when it is used to improve the digital image contrast. This is so because it redistributes pixels uniformly. Edge sharpening of the features that appear in a digital image can be successfully executed using kernel high- pass filter. On the other hand kernel low –pass filter always provides good results when image smoothing is required. It is also found that unsupervised classification can be used when there is less information about the data before classification. In addition to that good classification results can be obtained when the study area is partly familiar. In such cases supervised classification represents a better choice.en_US
dc.description.sponsorshipSudan University of Science and Technologyen_US
dc.language.isoenen_US
dc.publisherSudan University of Science and Technologyen_US
dc.subjectGeodesyen_US
dc.subjectGISen_US
dc.subjectDigital Image Processingen_US
dc.subjectRemote Sensingen_US
dc.titleDigital Image Processing in Remote Sensingen_US
dc.title.alternativeمعالجة الصور الرقمية في الاستشعار عن بعدen_US
dc.typeThesisen_US
Appears in Collections:Masters Dissertations : Engineering

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