Paper Info
Title
An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s12145-017-0298-2
Earth Science Informatics
Keywords
Field
DocType
Hyperspectral images,Spectral-spatial classification,Segmentation,Wavelet thresholding,Band selection,Support vector machines
Data mining,Computer science,Remote sensing application,Artificial intelligence,Spatial classification,Computer vision,Band selection,Pattern recognition,Wavelet thresholding,Segmentation,Support vector machine,Hyperspectral imaging,Pixel
Journal
Volume
Issue
ISSN
10
3
1865-0473
Citations 
PageRank 
References 
2
0.37
20
Authors
2
Name
Order
Citations
PageRank
Amin Zehtabian1183.18
Hassan Ghassemian239634.04