| Full text | |
| Author(s): |
Total Authors: 4
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| Affiliation: | [1] Univ Fed Sao Carlos, Dept Comp Sci, BR-13565905 Sao Carlos, SP - Brazil
[2] Sao Paulo State Univ, Dept Comp, BR-01049010 Sao Paulo - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | IEEE Geoscience and Remote Sensing Letters; v. 13, n. 5, p. 735-739, MAY 2016. |
| Web of Science Citations: | 1 |
| Abstract | |
Contextual-based learning aims at considering neighboring pixels to improve pixelwise-oriented classification techniques. In this letter, we presented a metaheuristic framework for the optimization of nondiscrete Markovian models considering the optimum-path forest (OPF) classifier, and we proposed a post-processing procedure to avoid overcorrection over high-frequency regions. The proposed approach outperformed previous results obtained with standard OPF in satellite imagery. (AU) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 12/06472-9 - Exploring Contextual Classification Approaches for Optimum-Path Forest |
| Grantee: | Daniel Osaku |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 13/20387-7 - Hyperparameter optimization in deep learning arquitectures |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Scholarships abroad - Research |