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Noise Equivalent Beta Naught is the different noise influence that beneficence to the radar signal. This type of noise is available in TerraSAR-X satellite images and expressed in forms of scaled polynomial described the noise power. On the other hand, Sigma naught or backscattering coefficient represents the average reflectivity of a horizontal material samples which used to reflect the nature of the land use and land cover in radar images. In this paper, radar satellite images in dual VV and HH polarization were used to study the influence of the noise on backscattering image classification. The result demonstrated that the visual interpretation of sigma naught which is result from the comparison between existence case and absence case (in the other word: with and without noise) of the noise illustrated that there is no different between them. In the other hand, for more details and more precise, an example of small images are used to show the values of obtained backscattering. The result demonstrated that the NEBN plays the main roles in decreasing the values of backscattering coefficient in TSX image. The influence of this noise had usually high in water body surface, because this surface is generally having small backscattering coefficients compared with land cover.

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.

LULC factors in Tikrit city (Iraq) and the neighboring municipalities are studied among 1989, 2002 and 2015 using various techniques of remote sensing, geographical information system (GIS), and fuzzy analytical hierarchy process (FAHP). Satellite imagery with GIS helped to assess the standard LULC changes in the long term period. FAHP permitted estimating the importance of various LULC by determination of the suitable weight for used factors and then producing the evaluating models. Using different techniques, two models were created (1) to estimate the degradation of the land (2) is generated to determine the reclamation of the area. The finding reveals that the a overall accuracy of 97.0939%, 98.9199% and 99.5817% or 1989, 2002 and 2015 respectively. The outcomes also revealed that urban, vegetation, and water features area are developed in the long term (1989–2015) about 4.35%, 4.28%, and 1.49%, respectively, while barren area is reduced about 5.57%.The degradation map index showed that the lands strongly debased are these converted from vegetation to barren, followed by moderate to high these changed from water areas to urban, while moderate degradation is noticed of urban transformed to barren soil. Contrary, the reclamation map index illustrated that the lands are powerfully transformed from barren to the vegetation and followed by those converted from barren to the water, while barren transformed to the urban is marked as moderate reclamation. The transformation from urban to vegetation or water was classified as the low and deficient class to evaluate the area. The study is also revealed that the integration of remote sensing and GIS produces a successful method for LULC monitoring and managing the environment.