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Extinction Profiles for the Classification of Remote Sensing Data

Extinction Profiles for the Classification of Remote Sensing Data

Title: Extinction Profiles for the Classification of Remote Sensing Data
Author: Ghamisi, Pedram
Souza, Roberto   orcid.org/0000-0001-7436-0058
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Zhu, Xiao Xiang
Rittner, Leticia   orcid.org/0000-0001-8182-5554
Date: 2016-07-18
Language: English
Scope: 1-15
University/Institute: Háskóli Íslands
University of Iceland
School: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Department: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
ISSN: 0196-2892
Subject: Data mining; Earth; Feature extraction; Radio frequency; Remote sensing; Spatial resolution; Random forest (RF); Image classification; Extinction profile (EP); Attribute profile (AP)
URI: https://hdl.handle.net/20.500.11815/61

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P. Ghamisi; R. Souza; J. A. Benediktsson; X. X. Zhu; L. Rittner; R. A. Lotufo, "Extinction Profiles for the Classification of Remote Sensing Data," in IEEE Transactions on Geoscience and Remote Sensing , vol.PP, no.99, pp.1-15 doi: 10.1109/TGRS.2016.2561842


Email Print Request Permissions 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.

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