Incremental classification algorithm of hyperspectral remote sensing images based on spectral-spatial information

被引：0
作者：
Wang, Junshu [1 ,2 ]
Jiang, Nan [1 ,2 ]
Zhang, Guoming [3 ]
Li, Yang [1 ,2 ]
Lü, Heng [1 ,2 ]
机构：
[1] Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing
[2] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
[3] Center of Health Statistics and Information of Jiangsu Province, Nanjing
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2015年 / 44卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Hyperspectral remote sensing image; Incremental classification; Morphology; Spatial information; Spectral information;
D O I：
10.11947/j.AGCS.2015.20140388
中图分类号：
学科分类号：
摘要：
An incremental classification algorithm INC_SPEC_MPext 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 (MPext). Combine spectral and MPext 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, MPext and the combination of sepctral and MPext. ©, 2015, SinoMaps Press. All right reserved.
引用
收藏
页码：1003 / 1013
页数：10
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