基于光譜相似度量的高光譜圖像多任務(wù)聯(lián)合稀疏光譜解混方法
doi: 10.11999/JEIT160011
基金項(xiàng)目:
中國(guó)地質(zhì)調(diào)查局地質(zhì)調(diào)查項(xiàng)目(1212011120226)，國(guó)家863計(jì)劃(2012AA12A308)，中國(guó)科學(xué)院科技服務(wù)網(wǎng)絡(luò)計(jì)劃項(xiàng)目(KFJ- EW-STS-046)
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)
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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(lián)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(wù)聯(lián)合稀疏解混方法。通過(guò)高光譜圖像的光譜特性統(tǒng)計(jì)值設(shè)定光譜度量閾值，對(duì)高光譜圖像中相似的像元光譜進(jìn)行光譜相似性度量分組，再對(duì)分組像元光譜數(shù)據(jù)進(jìn)行多任務(wù)聯(lián)合稀疏光譜解混模型的構(gòu)建和求解，得到最終的豐度系數(shù)。模擬數(shù)據(jù)實(shí)驗(yàn)結(jié)果表明，該方法一定程度上提升了現(xiàn)有聯(lián)合稀疏光譜解混方法的豐度估計(jì)精度，真實(shí)數(shù)據(jù)結(jié)果也驗(yàn)證了方法的有效性。
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關(guān)鍵詞:
- 高光譜圖像 /
- 光譜解混 /
- 聯(lián)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. -
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