AUTOMATIC INDICES BASED CLASSIFICATION METHOD FOR MAP UPDATING USING VHR SATELLITE IMAGES | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 6, Volume 48, No 5, September and October 2020, Page 845-868 PDF (2.98 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2020.114602 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohamed Fawzy  1; Yasser G. Mostafa 2; Farag Khodary1
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| 1Civil Eng. Dept., Faculty of Engineering, South Valley University, Qena, Egypt | ||||
| 2Civil Eng. Dept., Faculty of Engineering, Sohag University, Sohag, Egypt | ||||
| Abstract | ||||
| 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%). | ||||
| Keywords | ||||
| Feature extraction; satellite image; spectral indices; map updating; worldview 2 | ||||
| References | ||||
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