基于低秩結(jié)構(gòu)提取的高光譜圖像壓縮表示
doi: 10.11999/JEIT150906
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2.
(第二炮兵工程大學(xué)信息工程系 西安 710025) ②(清華大學(xué)電子工程系 北京 100084) ③(北京市遙感信息研究所 北京 100192)
國家自然科學(xué)基金(61132007, 61202332, 61503405),國家自然科學(xué)青年基金(61403397),中國博士后科學(xué)基金(2012M521905),陜西省自然科學(xué)基礎(chǔ)研究計(jì)劃項(xiàng)目(2015JM6313)
Low-rank Structure Based Hyperspectral Compression Representation
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2.
(Xi&rsquo
The National Natural Science Foundation of China (61132007, 61202332, 61503405), The National Natural Science Foundation for Young Scientists of China (61403397), China Postdoctoral Science Foundation (2012M521905), Natural Science Foundation of Shaanxi Province, China (2015JM6313)
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摘要: 為實(shí)現(xiàn)高效、精準(zhǔn)的高光譜圖像分類,該文利用低秩矩陣恢復(fù)從原始數(shù)據(jù)中提取低維特征,實(shí)現(xiàn)高光譜圖像的壓縮表示。針對高光譜應(yīng)用的特殊性,該文算法基于結(jié)構(gòu)相似性度量(Structural Similarity Index Measurement, SSIM)對矩陣恢復(fù)過程提出了信噪分離約束,有助于選擇更優(yōu)的模型參數(shù),增強(qiáng)表示的準(zhǔn)確性。實(shí)驗(yàn)證明,相比現(xiàn)有相關(guān)方法,該文算法能夠有效去除高光譜圖像中的噪聲,表示結(jié)果更為魯棒;在僅使用低維特征時(shí),仍能達(dá)到較高的分類精度。
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關(guān)鍵詞:
- 高光譜圖像分類 /
- 壓縮表示 /
- 低秩矩陣恢復(fù)
Abstract: A method which makes use of structure information abstracted from hyperspectral data via low-rank matrix recovery for hyperspectral image classification is proposed in this paper. The principle of maximizing structure information based on Structural Similarity Index Measurement (SSIM) is proposed to restrain the process of matrix recovery as well, which facilitates the separation of the signal and the noise. The experiments show that the proposed algorithm can effectively eliminate the non-linear noise in hyperspectral image and abstract the low-rank characteristics of hyperspectral image, which achieves better performance in classification. -
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