A simulated annealing feature extraction approach for hyperspectral images

Yang Lang Chang, Jyh Perng Fang, Jin Nan Liu, Hsuan Ren, Wen Yew Liang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

6 引文 斯高帕斯(Scopus)

摘要

In this paper, a novel study is proposed for the feature extraction of high volumes of remote sensing images by using a simulated annealing feature extraction (SAFE) approach. For hyperspectral imagery, complete modular eigenspace (CME) has been developed by clustering highly correlated hyperspectral bands into a smaller subset of band modular based on greedy algorithm. Instead of greedy paradigm as adopted in CME approach, this paper introduces a simulated annealing (SA) approach for hyperspectral imagery. It presents a framework which consists of three algorithms, referred to as SAFE, CME and the feature scale uniformity transformation (FSUT). SAFE selects the sets of non-correlated hyperspectral bands based on SA algorithm while utilizing the inherent separability of different classes in hyperspectral images to reduce dimensionality and further to effectively generate a unique CME feature. The proposed SA features avoids the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis and provides a fast procedure to simultaneously select the most significant features according to a scheme of SA. The experimental results show that the SAFE approach is effective and can be used as an alternative to the existing feature extraction algorithms.

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主出版物標題2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
頁面3190-3193
頁數4
DOIs
出版狀態已出版 - 2007
事件2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain
持續時間: 23 6月 200728 6月 2007

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)

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???event.eventtypes.event.conference???2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
國家/地區Spain
城市Barcelona
期間23/06/0728/06/07

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