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.
The production of Land Use and Land Cover thematic maps using remote sensing data is one of the things that must be dealt with carefully to obtain accurate results, data is obtained from sensors of different characteristics. It is not possible to obtain high spatial and spectral accuracy in one image, so we used a fusion image (multispectral image with a low spatial resolution with a panchromatic image with high spatial resolution), which achieved high efficiency in improving the methods of producing Land Use and Land Cover maps. In this study, we used Landsat-8 multispectral and panchromatic images. The study aims to investigate the effectiveness of panchromatic images in improving the methods of producing Land Use and Land Cover maps for the city of Karbala, Iraq. The Support Vector Machine was used to classify the fusion images using the Brovey method and Gram-Schmidt sharpening algorithms. The appropriate methodology for producing Land Use and Land Cover maps was suggested by comparing classifying results and the classification accuracy was evaluated through the confusion matrix. Where the results showed that the method of classifying the fused image by Gram-Schmidt and classified by Support Vector Machine is the best way to produce Land use and Land cover maps for the study area and achieved the highest results for overall accuracy and kappa coefficient of 97.81% and 0.95, respectively.
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.
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