基于光譜相似度量的高光譜圖像多任務聯合稀疏光譜解混方法

許寧; 尤紅建; 耿修瑞; 曹銀貴

doi:10.11999/JEIT160011

基于光譜相似度量的高光譜圖像多任務聯合稀疏光譜解混方法

doi: 10.11999/JEIT160011 cstr: 32379.14.JEIT160011

基金項目:

中國地質調查局地質調查項目(1212011120226)，國家863計劃(2012AA12A308)，中國科學院科技服務網絡計劃項目(KFJ- EW-STS-046)

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- 文章訪問數: 2164
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- 被引次數: 0
出版歷程
- 收稿日期: 2016-01-04
- 修回日期: 2016-06-06
- 刊出日期: 2016-11-19

Multi-task Jointly Sparse Spectral Unmixing Method Based on Spectral Similarity Measure of Hyperspectral Imagery

Funds:

The Geological Survey Program of China Geological Survey (1212011120226), The National 863 Program of China (2012AA12A308), The Science and Technology Services Network Program of Chinese Academy of Sciences (KFJ-EW- STS-046)

摘要

摘要: 基于圖像中存在的鄰域以及非局部相似等圖像空間特征和聯合稀疏解混思想，該文提出一種基于高光譜圖像光譜相似性度量的多任務聯合稀疏解混方法。通過高光譜圖像的光譜特性統(tǒng)計值設定光譜度量閾值，對高光譜圖像中相似的像元光譜進行光譜相似性度量分組，再對分組像元光譜數據進行多任務聯合稀疏光譜解混模型的構建和求解，得到最終的豐度系數。模擬數據實驗結果表明，該方法一定程度上提升了現有聯合稀疏光譜解混方法的豐度估計精度，真實數據結果也驗證了方法的有效性。
- 高光譜圖像 /
- 光譜解混 /
- 聯合稀疏表示 /
- 光譜相似性度量
Abstract: In this paper, a multi-task jointly sparse spectral unmixing method based on spectral similarity measure of hyperspectral imagery is proposed, which is a refinement of collaborative sparse spectral unmixing method. First, a threshold value is obtained through the statistical characters of some random selected neighboring pixels in hypersepctral image. Second, all pixels of hyperspectral image are grouped by a spectral similarity measure and the threshold value. Then, a multi-task jointly sparse optimization problem is constructed and solved for the grouped pixels, and the abundance coefficients are obtained finally. Experimentals results on synthetic and real hyperspectral image demonstrate the effectiveness of the proposed approach.
- Hyperspectral imagery /
- Spectral unmixing /
- Joint sparse representation /
- Spectral similarity measure

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參考文獻(33)

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