| Full text | |
| Author(s): |
Ghamisi, Pedram
;
Rasti, Behnood
;
Zhu, Xiao X.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS); v. N/A, p. 4-pg., 2017-01-01. |
| Abstract | |
To improve the classification of hyperspectral images, this paper proposes an approach for multi-sensor data fusion of LiDAR and hyperspectral data using extinction profiles and Orthogonal Total Variation Component Analysis (OTVCA). Results on the benchmark Houston data indicate the superior performance of the proposed approach compared to other approaches used in the experiments based on classification accuracies. (AU) | |
| FAPESP's process: | 15/12127-0 - Max-trees applied to medical images segmentation |
| Grantee: | Roberto Medeiros de Souza |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| FAPESP's process: | 13/23514-0 - Max-Tree: theory, algorithms and applications |
| Grantee: | Roberto Medeiros de Souza |
| Support Opportunities: | Scholarships in Brazil - Doctorate |