The Classification Accuracy of Multiple-Metric Learning Algorithm on Multi-Sensor Fusion
Firouz Abdullah Al-Wassai1, N.V. Kalyankar2
1Firouz Abdullah Al-Wassai, Department of Computer Science, (SRTMU), Nanded, India.
2N.V. Kalyankar, Principal, Yeshwant Mahavidyala College, Nanded, India.
Manuscript received on August 04, 2013. | Revised Manuscript received on August 28, 2013. | Manuscript published on September 05, 2013. | PP: 124-131 | Volume-3, Issue-4, September 2013. | Retrieval Number: D1791093413/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper focuses on two main issues; first one is the impact of Similarity Search to learning the training sample in metric space, and searching based on supervised learning classification. In particular, four metrics space searching are based on spatial information that are introduced as the following; Chebyshev Distance (CD); Bray Curtis Distance (BCD); Manhattan Distance (MD) and Euclidean Distance(ED) classifiers. The second issue investigates the performance of combination of multi-sensor images on the supervised learning classification accuracy. QuickBird multispectral data (MS) and panchromatic data (PAN) have been used in this study to demonstrate the enhancement and accuracy assessment of fused image over the original images. The supervised classification results of fusion image generated better than the MS did. QuickBird and the best results with ED classifier than the other did.
Keywords: Similarity Search, Metric Spaces, Distance Classifier, Image Fusion, Classification, Accuracy Assessment.