An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios

An adaptive framework for spectral-spatial classification based on a combination of pixel-based... Remotely sensed image analysis using spectral-spatial information plays a key role in modern remote sensing applications. This article presents a new semi-automatic framework for spectral-spatial classification of hyperspectral images. The proposed framework benefits from a combination of pixel-based and object-based classification scenarios in which the main parameters are adaptively tuned. In order to reduce the complexity of the method, an unsupervised band selection technique is used as well. Meanwhile, the wavelet thresholding is applied in order to smooth the selected bands. The classification results after applying the proposed method to well-known standard hyperspectral datasets are better than those of the most of the other state-of-the-art approaches. As an example, the overall classification accuracy achieved by applying the proposed semi-automatic spectral-spatial classification framework to the Salinas dataset is more than 99% for 10% training samples per class. Moreover, the vital parameters are adaptively set in our approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Earth Science Informatics Springer Journals

An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Earth Sciences; Earth Sciences, general; Information Systems Applications (incl.Internet); Simulation and Modeling; Ontology
ISSN
1865-0473
eISSN
1865-0481
D.O.I.
10.1007/s12145-017-0298-2
Publisher site
See Article on Publisher Site

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