Remotely sensed imagery identifying as the best type of data has information throughout the world. The imagery has a importance information, since it can show up-date-to day information, and provide a truly information. Different kinds of classifiers have been used to perform that. However, there is no once test for Land cover and Land use in Hilla city. The study aims to create land use classification by making a comparison between different algorithms in Hilla city, Babylon, Iraq. The WorldView-2 imagery is used to perform the per-processing, analysing of our comparison. Under the steps of pre-processing, the several corrections were made and performed on the imagery. For processing stages, two approaches were used; (1) Support Vector Machine and (2) Maximum Likelihood. The result reveals, that the Support Vector Machine method has the most significant of overall accuracy equal to 94.48% with kappa coefficient equal to 0.90, and these values much better and higher than those of Maximum Likelihood algorithm in estimating and extracting of Land cover/Land use. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area.

Feature extraction plays an important role in pattern recognition because band-to-band registration and geometric correction from different satellite images have linear image distortion. However, new near-equatorial orbital satellite system (NEqO) images is different because they have nonlinear distortion. Conventional techniques cannot overcome this type of distortion and lead to the extraction of false features and incorrect image matching. This research presents a new method by improving the performance of the Scale-Invariant Feature Transformation (SIFT) with a significantly higher rate of true extracted features and their correct matching. The data in this study were obtained from the RazakSAT satellite covering a part of Penang state, Malaysia. The method consists of many stages: image band selection, image band compression, image sharpening, automatic feature extraction, and applying the sum of absolute difference algorithm with an experimental and empirical threshold. We evaluate a refined features scenario by comparing the result of the original extracted SIFT features with corresponding features of the proposed method. The result indicates accurate and precise performance of the proposed method from removing false SIFT extracted features of satellite images and remain only true SIFT extracted features, that leads to reduce the extracted feature from using three frame size: (1) from 2000 to 750, 552 and 92 for the green and red bands image, (2) from 678 extracted control points to be 193, 228 and 73 between the green and blue bands, and (3) from 1995 extracted CPs to be 656, 733, and 556 between the green and near-infrared bands, respectively.

Atmospheric correction is a main problem in visible or near-infrared remote sensing images since the existence of the atmosphere continuously influences the radiation from the ground to the sensor. Hence, atmospheric correction is necessary. Remotely sensed imagery has noise affected by atmospheric particles that can unclear the image and make quantitative analysis unreliable. The aim of this research is to evaluate atmospheric correction methods for remotely sensed imagery using ENVI software to get accurate results. In this research, three methods of atmospheric correction have been selected depend on the essential parameters whether from the image or field for hyperspectral imagery by using ENVI software. These methods include: IAR Reflectance, Flat field correction, and Empirical Line Correction. The results showed the corrected images from three methods give better interpretation of Z-profile than original image which helps to determine the absorption feature and increase the possibility to get a good result after processing. IAR Reflectance method gives easier spectral curve to be interpreted when comparison between radiance spectrum and reflectance spectra than other correction methods.

In this research support vector machine (SVM) method apply to classify the satellite image and produce land use and land cover (LULC) map. The used data is the multispectral Landsat-8 OLI satellite image with a spatial resolution of (30 × 30)m ². However, the Karbala city was the study area. The SVM Applied with the default parameters of Kernel type, gamma in kernel function, penalty parameter and classification probability threshold. The SVM method achieved high accuracy in separating the categories of the study area based on the test samples collected from the study area in the Karbala province, Iraq. The classification training sites were selected based on visual interpretation and Google Earth Program. The image classification carried for six classes of the study area (Urban Area, Vegetation Area, Soil-1, Soil-2, Water Bodies and Roads). The results show a good accuracy of using SVM method based on relying on the capabilities and the precision of each pixel within the categories. The result evaluation was performed using the confusion matrix, the Kappa coefficient and the overall were 0.89 and 90.61% respectively. The SVM method is able to classify the land use and land cover of the study area with good and accurate results.

Two common techniques for classifying satellite imagery are pixel-based and Feature extraction image analysis methods. Typically, for agreements reached imaging, pixel-based analysis is used, whereas high-resolution imagery is suitable for Feature extraction analysis. However, In the classification of moderate images, image segmentation's ability depending on criteria such as shape, color, texture, and spatial features in Feature extraction image analysis implies it can perform better than pixel-based analysis. A comparative study of the two methods was performed using Sentinel-2 imagery from 18 May 2020 to categorize LU/LC in the City of Baghdad, Iraq. After calculating LU/LC for Baghdad images' capital, a supervised classification was performed using the two methods. The images used have been the support vector machines (SVM) and the maximum likelihood classification (MLC) for pixel-based method and Feature extraction method, which is available in ENVI and ArcGIS software packages, respectively. Land cover and land use classes included five Groups (vegetation area, asphalt road, soil area, water body, and built-up) was found that the Feature extraction methodology produced higher overall accuracy and Kappa index in the city of Baghdad image. The highest achieved accuracy for the Feature extraction technique was overall accuracy 95% with Kappa index 0.94 of SVM and overall accuracy of 92% with Kappa index 0.90 of MLC. In comparison, the highest accuracy for the pixel-based was overall accuracy 88% with Kappa index 0.84 of SVM and overall accuracy 86% with Kappa index 0.82 of MLC.

Recently the problem of desertification and degradation of land become an environmental global challenge. Iraq is facing a relatively big problem of desertification in the past ten years, especially in the central and southern sections, and the problem has been exacerbated for several reasons. Geographic Information Systems (GIS) and satellite imagery play a critical role in developing global and local operational capacity and monitoring desertification in drylands. Based on these causes this research focused on the study of the state of desertification in a part of Al Najaf Governorate, with an area of (5143.40 km²), using remote sensing and (GIS). Sentinel 2 images were used for the study area during the period (2015-2021). The images were classified using a maximum likelihood method (MLC) algorithm. The change detection results revealed that the studied area is severely desertified especially in its western parts. It has suffered from wind erosion, dunes, and a lack of water resources.