Advanced search
Start date
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Extinction Profiles for the Classification of Remote Sensing Data

Full text
Ghamisi, Pedram ; Souza, Roberto ; Benediktsson, Jon Atli ; Zhu, Xiao Xiang ; Rittner, Leticia ; Lotufo, Roberto A.
Total Authors: 6
Document type: Journal article
Source: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING; v. 54, n. 10, p. 5631-5645, OCT 2016.
Web of Science Citations: 49

With respect to recent advances in remote sensing technologies, the spatial resolution of airborne and spaceborne sensors is getting finer, which enables us to precisely analyze even small objects on the Earth. This fact has made the research area of developing efficient approaches to extract spatial and contextual information highly active. Among the existing approaches, morphological profile and attribute profile (AP) have gained great attention due to their ability to classify remote sensing data. This paper proposes a novel approach that makes it possible to precisely extract spatial and contextual information from remote sensing images. The proposed approach is based on extinction filters, which are used here for the first time in the remote sensing community. Then, the approach is carried out on two well-known high-resolution panchromatic data sets captured over Rome, Italy, and Reykjavik, Iceland. In order to prove the capabilities of the proposed approach, the obtained results are compared with the results from one of the strongest approaches in the literature, i.e., APs, using different points of view such as classification accuracies, simplification rate, and complexity analysis. Results indicate that the proposed approach can significantly outperform its alternative in terms of classification accuracies. In addition, based on our implementation, profiles can be generated in a very short processing time. It should be noted that the proposed approach is fully automatic. (AU)

FAPESP's process: 15/12127-0 - Max-trees applied to medical images segmentation
Grantee:Roberto Medeiros de Souza
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 13/23514-0 - Max-Tree: theory, algorithms and applications
Grantee:Roberto Medeiros de Souza
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
Grantee:Fernando Cendes
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC