A multi-layer Classification Technique for High Resolution Satellite Images Using Radiometric Calibration Modelling | ||||
Journal of Engineering Science and Military Technologies | ||||
Article 5, Volume 2, Issue 1, January 2018, Page 36-43 PDF (1.12 MB) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/ejmtc.2017.574.1030 | ||||
View on SCiNiTO | ||||
Authors | ||||
mahmoud abdallah shwaky 1; Ahmed Elsharkawy2; Essam Hassan Hamza 3; Hassan Elsaid Elhifnawy4 | ||||
1egyptian armed forces | ||||
2Military technical college | ||||
3Military Technical Collage | ||||
4Civil engineering department | ||||
Abstract | ||||
Recent developments in satellite sensors tend to the availability of high spatial and spectral resolution images. The motivation of this research paper is to get maximum benefits of different bands in high resolution satellite images. In this research paper, a novel classification technique is introduced where the shared texture features properties problem is addressed. Atmospheric correction is applied on a high resolution World View 2 (WV2) image to produce reflectance value for all spectral bands. Reflectance image is produced by knowing the environmental parameters of the images at the capturing time, which can be extracted from auxiliary files associated with the input image. A multi-layer classification tree analysis is applied on a reflectance image to extract urban area features based on investigated thresholding values. The proposed technique is investigated through MATLAB environment. The results of the proposed technique are assessed versus classification results of Maximum Likelihood classification technique that is applied through ENVI software. The assessment of classification results is represented in confusion matrix format and determination of Kappa Coefficient. The investigated technique succeeded in classifying urban area features up to 90%. The proposed technique is fast, automated and suitable for any image with same spectral bands as WV2 satellite image. | ||||
Keywords | ||||
A multi-layer classification technique; assessment of classification; classification; Maximum likelihood (ML); Reflectance | ||||
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