Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/640705
Title: An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation
Authors: Zhou Zhang;Edoardo Pasolli;Melba M. Crawford;James C. Tilton
subject: spatial information|classification|hierarchical segmentation (HSeg)|Active learning (AL)|hyperspectral images
Year: 2016
Publisher: IEEE
Abstract: Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
URI: http://localhost/handle/Hannan/175092
http://localhost/handle/Hannan/640705
ISSN: 1939-1404
2151-1535
volume: 9
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7342892.pdf3.48 MBAdobe PDFThumbnail
Preview File
Title: An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation
Authors: Zhou Zhang;Edoardo Pasolli;Melba M. Crawford;James C. Tilton
subject: spatial information|classification|hierarchical segmentation (HSeg)|Active learning (AL)|hyperspectral images
Year: 2016
Publisher: IEEE
Abstract: Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
URI: http://localhost/handle/Hannan/175092
http://localhost/handle/Hannan/640705
ISSN: 1939-1404
2151-1535
volume: 9
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7342892.pdf3.48 MBAdobe PDFThumbnail
Preview File
Title: An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation
Authors: Zhou Zhang;Edoardo Pasolli;Melba M. Crawford;James C. Tilton
subject: spatial information|classification|hierarchical segmentation (HSeg)|Active learning (AL)|hyperspectral images
Year: 2016
Publisher: IEEE
Abstract: Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.
URI: http://localhost/handle/Hannan/175092
http://localhost/handle/Hannan/640705
ISSN: 1939-1404
2151-1535
volume: 9
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7342892.pdf3.48 MBAdobe PDFThumbnail
Preview File