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العنوان
Optical And Microwave Imagery For Land Cover Classification =
الناشر
Alexandria :
المؤلف
Khalil, Noha Mostafa Fakhry.
هيئة الاعداد
مشرف / محمد الراى
مشرف / صلاح مصباح
باحث / نهى مصطفى فخرى خليل
مناقش / احمد محمد على
الموضوع
Environmental Studies.
تاريخ النشر
2000 .
عدد الصفحات
167 P. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
علوم البيئة
تاريخ الإجازة
1/1/2000
مكان الإجازة
جامعة الاسكندريه - معهد الدراسات العليا والبحوث - Environmental Studies
الفهرس
Only 14 pages are availabe for public view

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from 182

Abstract

In Egypt, a number of areas have been suffering serious environmental problems and have been experiencing very rapid changes. The Rosetta area is one of them and is considered to be highly vulnerable from an environmental point of view. This follows naturally from its unique location and abundant resources, which render it one of the most promising areas for future development. These characteristics have made the study area an appropriate location for a variety of remote sensing studies focusing on land cover interpretation.
Remote sensing data with improved spatial, spectral and radiometric resolutions are currently available. In order to fully exploit the potential of various remote sensing data sources, synergistic analysis of different satellite images (different in terms of spectral/spatial resolution, geometry, etc.) is attempted based on the merits and limitations of each sensor’s data. Microwave imagery has been of special interest due to its all-weather, day/night capabilities. More importantly, microwave imagery provides a new and different view of the earth, thereby complementing visual and infrared images.
This is a study on multisensor analysis involving SPOT, ERS-1 and RADARSAT imagery. The aim was to investigate the possibility of fusing spaceborne microwave data with existing images from optical sensors for the purpose of enhancing interpretation and hence improving the process of information extraction. One of the key issues investigated was the use of several pixel-based image merging techniques and their effect on the classification performance.