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Proceedings Paper

Semi-supervised classification tool for DubaiSat-2 multispectral imagery
Author(s): Saeed Al-Mansoori
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Paper Abstract

This paper addresses a semi-supervised classification tool based on a pixel-based approach of the multi-spectral satellite imagery. There are not many studies demonstrating such algorithm for the multispectral images, especially when the image consists of 4 bands (Red, Green, Blue and Near Infrared) as in DubaiSat-2 satellite images. The proposed approach utilizes both unsupervised and supervised classification schemes sequentially to identify four classes in the image, namely, water bodies, vegetation, land (developed and undeveloped areas) and paved areas (i.e. roads). The unsupervised classification concept is applied to identify two classes; water bodies and vegetation, based on a well-known index that uses the distinct wavelengths of visible and near-infrared sunlight that is absorbed and reflected by the plants to identify the classes; this index parameter is called "Normalized Difference Vegetation Index (NDVI)". Afterward, the supervised classification is performed by selecting training homogenous samples for roads and land areas. Here, a precise selection of training samples plays a vital role in the classification accuracy. Post classification is finally performed to enhance the classification accuracy, where the classified image is sieved, clumped and filtered before producing final output. Overall, the supervised classification approach produced higher accuracy than the unsupervised method. This paper shows some current preliminary research results which point out the effectiveness of the proposed technique in a virtual perspective.

Paper Details

Date Published: 20 October 2015
PDF: 7 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460X (20 October 2015); doi: 10.1117/12.2195081
Show Author Affiliations
Saeed Al-Mansoori, Mohammed Bin Rashid Space Ctr. (United Arab Emirates)


Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)

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