الفهرس 
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Abstract
During the past few years, the number of earth observation satellites has increased, and the ability of
these satellites to scan ne details and characteristics of the Earth surface has also increased. These
details include the spatial, spectral, and temporal dimensions in their ne details. All this huge amount
of data is scanned and recorded in data centers around the world. Much of this data is now accessible
at a little cost or even o ered for free. This huge amount of data has become impossible to process and
analyze with classical computational ways. All this made the processing, analysis, and classi cation of
satellite images an indispensable necessity for the real bene t of satellites.
Therefore, this thesis provides an integrated framework for classifying the di erent types of satellite
images, using innovative methods to increase classi cation accuracy and increase the eciency of classi
cation performance compared to other modern and public methods. In terms of spatial resolution,
the proposed framework included classifying every high spatial-resolution image (less than 1 m) and
classifying proportionally, low-resolution (greater than 10 m) images. In terms of spectral resolution,
the proposed framework classi ed low spectral-resolution images with 3 traditional bands (red, green,
and blue) and medium-resolution spectral images with 10 bands, as well as hyperspectral images with
more than 100 bands. In terms of temporal resolution, we have used the classi cation of satellite images
during a single season several times to help in the classi cation process.
The proposed framework utilizes and customizes the state-of-the-art deep learning methods as well as
traditional methods for comparison and evaluation. A deep review and study have been carried out for
the di erent existing deep learning frameworks. These di erent frameworks exhibit di erent conceptions
to the examined problem, including the use of many neuron layers, types of layers (convolution, pooling,
etc.), and the type of connection between layers. As a customization process, we dealt with data
preparation to meet our remote sensing context as well as optimization of learning parameters such
as learning rate, loss functions, dropouts, etc. The proposed framework has been coded in Python
as a leading open-source language and utilized the up-to-date scienti c and learning libraries such as
Tensor
ow, Sci-kit-learn, and Numpy and we o er it to the scienti c community to use it build upon
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it. To succeed with deep learning methods, we had to have huge datasets for training, validation, and
testing. In this regard, we used the available benchmark data that have been reported in the literature
as well as our own remote sensing data for some real applications.
This framework has been applied in many practical and experimental applications. It has been applied
the proposed methods to classify the di erent types of agricultural cover in Fayoum Governorate in Egypt
using images of the Sentinel 2 and Landsat 8 satellites. It has, also, been applied to classify di erent
levels of urban areas in Greater Cairo through the Sentinel2 satellite imagery. In the object detection
context, the framework has been applied to high spatial-resolution images to detect and localize objects
like planes, cars, ships, etc. For spectrally rich images, a new method has been proposed to classify
vegetation cover using hyperspectral images.
All proposed methods have been conceptualized so that best performance, whether in terms of computational
speed or classi cation accuracy, is to be achieved. To achieve this, the standard evaluation
metrics have been used to evaluate methods in the proposed framework in di erent hardware and software
environments (desktops, cloud computers, Windows operating systems, Linux operating systems)
The results showed a remarkable superiority with the proposed framework methods.