Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information
An incremental classification algorithm INC_SPEC_MP<sub>ext</sub> was proposed for hyperspectral remote sensing images based on spectral and spatial information. The spatial information was extracted by building morphological profiles based on several principle components of hyperspectra...
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Surveying and Mapping Press
2015-09-01
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doaj-571547bd2c3946689d396afe3cf643792020-11-24T23:30:50ZzhoSurveying and Mapping PressActa Geodaetica et Cartographica Sinica1001-15951001-15952015-09-014491003101310.11947/j.AGCS.2015.2014038820150909Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial InformationWANG Junshu0JIANG Nan1ZHANG Guoming2LI Yang3LV Heng4Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;Center of Health Statistics and Information of Jiangsu Province, Nanjing 210008, ChinaKey Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China;An incremental classification algorithm INC_SPEC_MP<sub>ext</sub> was proposed for hyperspectral remote sensing images based on spectral and spatial information. The spatial information was extracted by building morphological profiles based on several principle components of hyperspectral image. The morphological profiles were combined together in extended morphological profiles (MP<sub>ext</sub>). Combine spectral and MP<sub>ext</sub> to enrich knowledge and utilize the useful information of unlabeled data at the most extent to optimize the classifier. Pick out high confidence data and add to training set, then retrain the classifier with augmented training set to predict the rest samples. The process was performed iteratively. The proposed algorithm was tested on AVIRIS Indian Pines and Hyperion EO-1 Botswana data, which take on different covers, and experimental results show low classification cost and significant improvements in terms of accuracies and Kappa coefficient under limited training samples compared with the classification results based on spectral, MP<sub>ext</sub> and the combination of sepctral and MP<sub>ext</sub>.http://html.rhhz.net/CHXB/html/2015-9-1003.htmhyperspectral remote sensing imagemorphologyspatial informationspectral informationincremental classification |
collection |
DOAJ |
language |
zho |
format |
Article |
sources |
DOAJ |
author |
WANG Junshu JIANG Nan ZHANG Guoming LI Yang LV Heng |
spellingShingle |
WANG Junshu JIANG Nan ZHANG Guoming LI Yang LV Heng Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information Acta Geodaetica et Cartographica Sinica hyperspectral remote sensing image morphology spatial information spectral information incremental classification |
author_facet |
WANG Junshu JIANG Nan ZHANG Guoming LI Yang LV Heng |
author_sort |
WANG Junshu |
title |
Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information |
title_short |
Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information |
title_full |
Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information |
title_fullStr |
Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information |
title_full_unstemmed |
Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information |
title_sort |
incremental classification algorithm of hyperspectral remote sensing images based on spectral-spatial information |
publisher |
Surveying and Mapping Press |
series |
Acta Geodaetica et Cartographica Sinica |
issn |
1001-1595 1001-1595 |
publishDate |
2015-09-01 |
description |
An incremental classification algorithm INC_SPEC_MP<sub>ext</sub> was proposed for hyperspectral remote sensing images based on spectral and spatial information. The spatial information was extracted by building morphological profiles based on several principle components of hyperspectral image. The morphological profiles were combined together in extended morphological profiles (MP<sub>ext</sub>). Combine spectral and MP<sub>ext</sub> to enrich knowledge and utilize the useful information of unlabeled data at the most extent to optimize the classifier. Pick out high confidence data and add to training set, then retrain the classifier with augmented training set to predict the rest samples. The process was performed iteratively. The proposed algorithm was tested on AVIRIS Indian Pines and Hyperion EO-1 Botswana data, which take on different covers, and experimental results show low classification cost and significant improvements in terms of accuracies and Kappa coefficient under limited training samples compared with the classification results based on spectral, MP<sub>ext</sub> and the combination of sepctral and MP<sub>ext</sub>. |
topic |
hyperspectral remote sensing image morphology spatial information spectral information incremental classification |
url |
http://html.rhhz.net/CHXB/html/2015-9-1003.htm |
work_keys_str_mv |
AT wangjunshu incrementalclassificationalgorithmofhyperspectralremotesensingimagesbasedonspectralspatialinformation AT jiangnan incrementalclassificationalgorithmofhyperspectralremotesensingimagesbasedonspectralspatialinformation AT zhangguoming incrementalclassificationalgorithmofhyperspectralremotesensingimagesbasedonspectralspatialinformation AT liyang incrementalclassificationalgorithmofhyperspectralremotesensingimagesbasedonspectralspatialinformation AT lvheng incrementalclassificationalgorithmofhyperspectralremotesensingimagesbasedonspectralspatialinformation |
_version_ |
1725540134871367680 |