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The earth surface changes continuously due to natural causes and human activities. New generations of satellite sensors, such as WorldView and GeoEye, provide new data to better delineate, track, and visualize changes in land cover. Several classes are used in the satellite images. All artifacts with elevations greater than the ground surface (buildings in particular) may appear in the wrong location. The correction of buildings position is an important task for mapping applications. The main aim of this study is to introduce a change detection approach using very high-resolution satellite images (VHR) for map updating. In this approach, an approximated method for building relief displacement correction was developed. In this paper, image preprocessing was carried out and the information content of the satellite image was evaluated. Then change detection between GeoEye-1 image and Sohag map was carried out using post-classification comparison technique. After that, the change map result was divided into two classes: building and non-building. All objects were transformed from raster to vector format. For building objects, the height was estimated. A python code was written to calculate relief displacement using buildings' height and shadow length. The vector layer was added to update the reference map. The results showed the ability of very high-resolution satellite images for updating large-scale maps in Egypt. Also, the approximated method for building relief displacement correction is a promising method. It has RMSE accuracy of 0.95m. [ABSTRACT FROM AUTHOR]

Urban land cover classification using Very High Resolution (VHR) satellite images is a very important source of information for map updating. Egyptian environment has more challenges in feature extraction. The main problem lies in the spectral similarity between different land cover classes. Also, great diversity in sizes, shapes, and materials of each class. The main aim of this work is to represent a new automatic indices-based classification method for map updating using VHR satellite images. The method uses a set of spectral indices with their thresholds in consecutive order, chosen based on WorldView-2 (WV-2) bands, to classify land cover in the Egyptian environment. For this study, WV-2 satellite images with eight spectral bands were used. The proposed method is compared with five traditional classification methods; Minimum distance, Spectral angle mapper, Mahalanobis distance, Spectral correlation mapper, and Maximum likelihood method, which included in ERDAS 2015 software, for validation purpose and checking its stability. The results show that the extracted features with the proposed method can contribute significantly to update Egyptian medium scale maps. The average overall accuracy achieved with the proposed approach (75.31%) is higher than those obtained using Minimum distance (54.0%), Spectral angle mapper (69.50%), and Mahalanobis distance (73.63%). Also, it is near to those obtained by the Spectral correlation mapper (76.50%), and Maximum likelihood method (78.25%). [ABSTRACT FROM AUTHOR]

Robust building detection from satellite images has been a subject of interest for several decades. Very High Resolution (VHR) satellite images support the efficient extraction of manmade objects. The main aim of this paper is to present an approach for building extraction from VHR satellite images for map updating in Egypt. To achieve this aim, a comparison of pixel and object-based classification techniques has been applied. Then, different refinement processes based on shadow, context, shape, and Digital Surface Model (DSM) data are carried out. Two study areas from the VHR satellite images for Assuit and Sohag cities are used. A comparison of the classification techniques shows that the Maximum Likelihood Classifier (MLC) for pixel-based technique and Support Vector Machine (SVM) for object-based technique give the highest overall accuracy results. Refinement based on shadow, context, shape, and DSM information improves the overall accuracy with an average of 18%. Thus, the building extraction results can contribute significantly to update maps in Egypt. [ABSTRACT FROM AUTHOR]

High and accelerating rate of the urban changes and extensions, in developing countries such as Egypt, calls for an efficient and fast technique for mapping. The availability of the new generation commercial one-meter resolution satellite images has opened a new era for producing and updating large-scale digital maps. The main objective of this study is to evaluate the potential of VHR satellite images for large scale mapping in Egypt. Data used in this study are IKONOS-2 images (Panchromatic (1m) and Multispectral (4m)) acquired on 2006 and topographic map dated 2002 at scale 1:5000 of Assiut area. In this paper, an investigation is carried out for the potential of the information content in pan- sharpened IKONOS image. Then, the classification process is carried out with object-based method . The classified image has been converted to vector format. After that, an investigation is carried out for these vectors through overlaid it to the available old map. The result showed that the information content of IKONOS images has the capability of updating of 1: 5000 maps for good planned area , while, that ability will be decreases with decreasing the degree of planning. [ABSTRACT FROM AUTHOR]