Deriving land cover information from satellite data is one of the most common applications employed to monitor, evaluate, and manage the environment. This study aims to detect the land cover/land use changes and calculate the areas of different land cover types in Baghdad, Iraq, for the period from 2015 to 2020, using Landsat 8 images. The supervised Maximum Likelihood Classification (MLC) method was applied to classify the images. Four land cover types were obtained, namely urban, vegetation, water, and barren soil.  Changes in the four land cover classes during the study period were observed. The extent of the urban, vegetation, and water areas was increased by about 7.5%, 9.5%, and 1.5%, respectively, whereas t

In this paper, we introduce a new complex integral transform namely ”Complex Sadik Transform”. The
properties of this transformation are investigated. This complex integral transformation is used to reduce
the core problem to a simple algebraic equation. The answer to this primary problem can than be obtained
by solving this algebraic equation and applying the inverse of complex Sadik transformation. Finally,
the complex Sadik integral transformation is applied and used to find the solution of linear higher order
ordinary differential equations. As well as, we present and discuss, some important real life problems
such as: pharmacokinetics problem ,nuclear physics problem and Beams Probem

Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximu

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Image compression is very important in reducing the costs of data storage transmission in relatively slow channels. Wavelet transform has received significant attention because their multiresolution decomposition that allows efficient image analysis. This paper attempts to give an understanding of the wavelet transform using two more popular examples for wavelet transform, Haar and Daubechies techniques, and make compression between their effects on the image compression.

    The Ozone Monitoring Instrument (OMI) measures the reflected solar radiation in the ultraviolet and visible part in the spectral range that is between 270 and 500 nm, using two channels with a spectral resolution of about 0.5 nm. Ground-level tropospheric ozone is one of the air pollutants of most concern. In the troposphere, near the Earth's surface, human activities lead to ozone concentrations several times higher than the natural background level. To evaluate the ozone distribution over Iraq, the ozone data from OMI were analyzed using geostatistical techniques. Theoretical spherical models provided the best fit for all monthly experimental variograms. The parameters of these variograms (sill, range and nugget) wer

The process of combining the significant information from a series of images into a single image called image sharpening or image fusing, where the resultant fused image will be having more spatial and spectral information than any of the input images. in this research two images of the same place in different spatial resolution have been used the first one was panchromatic and the second image was multispectral with spatial resolution 0.5m and 2 m respectively. These images were captured by world view-2 sensor. This research resent four pan sharpening methods like (HSV, Brovey (color normalizes) , Gram shmidt and PCA)these methods were used to combine the adopted images to get multispectral image

     Let R be an associative ring. The essential purpose of the present paper is to introduce the concept of generalized commuting mapping of R. Let U be a non-empty subset of R, a mapping   : R  R is called a generalized commuting mapping on U if there exist a mapping :R R such that =0, holds for all U. Some results concerning the new concept are presented.