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題 名 | A Fast Classification Scheme for Hyperspectral Imagery=一個超高維頻譜影像快速分類的方法 |
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作 者 | 杜德銘; 陳進興; | 書刊名 | Proceedings of the National Science Council : Part A, Physical Science and Engineering |
卷 期 | 22:4 1998.07[民87.07] |
頁 次 | 頁456-465 |
分類號 | 448.5 |
關鍵詞 | 超高維頻譜影像; 快速分類方法; Canonical analysis; Band selection; Recursive maximum likelihood classifier; Winograd's identity; |
語 文 | 英文(English) |
中文摘要 | 在本文中,我們針對超高維頻譜影像的像元級分類,提出一個比傳統式最大相似 分類器快數十至數百倍的兩級式分類方法。首先在第一級,我們將負荷因子的觀念融入正規 分析技術中,發展出一種有效的頻帶選取方法,它可以計量出每一頻帶的分類能力,讓我們 剔除有資訊贅餘的頻帶而保留對分類有用的頻帶。接著在第二級,我們設計了一個運用 Winograd法則的遞迴式最大相似分類器,藉以滅少分類的時間 |
英文摘要 | Classification for high dimensional remote sensing data generally requires a large set of data samples and enormous processing time, especially for hyperspectral image data. In this paper, a fast classification scheme is presented. The first stage of process is to develop a strategy for band selection which is designed based on the canonical analysis (CA) and the concept of loading factors to weigh bands in accordance with their energies. The suggested band selection algorithm allows one to predetermine which bands will be used for data processing so that data dimensionality is greatly reduced. It is then followed by a second stage using a maximum likelihood (ML) classifier which is recursive and designed based on Winograd's algorithm to achieve computational efficiency. The experimental results show that the proposed fast classification scheme reduces the computing time by a factor of 27 to 107 compared to the conventional one-stage ML classifier. |
本系統中英文摘要資訊取自各篇刊載內容。