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A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles

Titill: A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles
Höfundur: Ghamisi, Pedram
Dalla Mura, Mauro   orcid.org/0000-0002-9656-9087
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Útgáfa: 2015
Tungumál: Enska
Umfang: 2335-2353
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
ISSN: 0196-2892
DOI: DOI: 10.1109/TGRS.2014.2358934
Efnisorð: Data mining; Feature extraction; Hyperspectral imaging; Image reconstruction; Shape
URI: https://hdl.handle.net/20.500.11815/64

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P. Ghamisi, M. Dalla Mura and J. A. Benediktsson, "A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles," in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2335-2353, May 2015.


Just over a decade has passed since the concept of morphological profile was defined for the analysis of remote sensing images. Since then, the morphological profile has largely proved to be a powerful tool able to model spatial information (e.g., contextual relations) of the image. However, due to the shortcomings of using the morphological profiles, many variants, extensions, and refinements of its definition have appeared stating that the morphological profile is still under continuous development. In this case, recently introduced theoretically sound attribute profiles (APs) can be considered as a generalization of the morphological profile, which is a powerful tool to model spatial information existing in the scene. Although the concept of the AP has been introduced in remote sensing only recently, an extensive literature on its use in different applications and on different types of data has appeared. To that end, the great amount of contributions in the literature that address the application of the AP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, and hyperspectral) proves how the AP is an effective and modern tool. The main objective of this survey paper is to recall the concept of the APs along with all its modifications and generalizations with special emphasis on remote sensing image classification and summarize the important aspects of its efficient utilization while also listing potential future works.


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