| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] INPE, DPI, BR-12227010 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 1
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| Document type: | Review article |
| Source: | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING; v. 87, p. 241-248, JAN 2014. |
| Web of Science Citations: | 14 |
| Abstract | |
Several remote sensing studies have adopted the Support Vector Machine (SVM) method for image classification. Although the original formulation of the SVM method does not incorporate contextual information, there are different proposals to incorporate this type of information into it. Usually, these proposals modify the SVM training phase or make an integration of SVM classifications using stochastic models. This study presents a new perspective on the development of contextual SVMs. The main concept of this proposed method is to use the contextual information to displace the separation hyperplane, initially defined by the traditional SVM. This displaced hyperplane could cause a change of the class initially assigned to the pixel. To evaluate the classification effectiveness of the proposed method a case study is presented comparing the results with the standard SVM and the SVM post-processed by the mode (majority) filter. An ALOS/PALSAR image, PLR mode, acquired over an Amazon area was used in the experiment. Considering the inner area of test sites, the accuracy results obtained by the proposed method is better than SVM and similar to SVM post-processed by the mode filter. The proposed method, however, produces better results than mode post-processed SVM when considering the classification near the edges between regions. One drawback of the method is the computational cost of the proposed method is significantly greater than the compared methods. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 08/58112-0 - Land use change in Amazonia: institutional analysis and modeling at multiple temporal and spatial scales |
| Grantee: | Maria Isabel Sobral Escada |
| Support Opportunities: | Research Program on Global Climate Change - Thematic Grants |
| FAPESP's process: | 08/57719-9 - Program on Climate Change - INCT CLIMA |
| Grantee: | Carlos Afonso Nobre |
| Support Opportunities: | Research Program on Global Climate Change - Thematic Grants |