一種聯(lián)合Khatri-Rao子空間與塊稀疏壓縮感知的差分SAR層析成像方法
doi: 10.11999/JEIT160222
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2.
(中國(guó)科學(xué)院電子學(xué)研究所微波成像技術(shù)國(guó)家級(jí)重點(diǎn)實(shí)驗(yàn)室 北京 100190) ②(中國(guó)科學(xué)院大學(xué) 北京 100049) ③(中國(guó)資源衛(wèi)星應(yīng)用中心 北京 100094)
基金項(xiàng)目:
國(guó)家發(fā)改委衛(wèi)星及應(yīng)用產(chǎn)業(yè)發(fā)展專項(xiàng)項(xiàng)目 (發(fā)改委高技[2012]2083號(hào))
Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing
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2.
(National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
Funds:
The National Development and Reform Commission Satellite and Application Development Projects of China [2012] 2083
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摘要: 雖然采用壓縮感知技術(shù)(Compressive Sensing, CS)的差分SAR層析成像方法實(shí)現(xiàn)了4維空間信息的重構(gòu)，但是此方法僅利用了目標(biāo)的稀疏特性并沒(méi)有考慮目標(biāo)的結(jié)構(gòu)特性，因此對(duì)同時(shí)具有稀疏特性和結(jié)構(gòu)特性的目標(biāo)進(jìn)行重構(gòu)時(shí)其性能較差。針對(duì)這一問(wèn)題，該文采用聯(lián)合Khatri-Rao子空間和塊壓縮感知(Khatri-Rao Subspace and Block Compressive Sensing, KRS-BCS)，提出一種差分SAR層析成像方法。該方法依據(jù)目標(biāo)的結(jié)構(gòu)特性和重構(gòu)觀測(cè)矩陣具有的Khatri-Rao積性質(zhì)，將稀疏結(jié)構(gòu)目標(biāo)的差分SAR層析成像問(wèn)題轉(zhuǎn)化為Khatri-Rao子空間下的BCS問(wèn)題，最后對(duì)目標(biāo)進(jìn)行塊稀疏的l1/l2 范數(shù)最優(yōu)化求解。相比CS差分SAR層析成像方法，該方法不僅保持了CS差分SAR層析成像方法的高分辨率特點(diǎn)，而且其重構(gòu)精度更高性能更優(yōu)。仿真數(shù)據(jù)和ENVISAT星載ASAR數(shù)據(jù)以及地面GPS實(shí)測(cè)數(shù)據(jù)的試驗(yàn)結(jié)果驗(yàn)證了該方法的有效性。
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關(guān)鍵詞:
- 差分SAR層析成像技術(shù) /
- Khatri-Rao子空間 /
- 塊壓縮感知
Abstract: While the use of differential SAR tomography based on Compressive Sensing (CS) makes it possible to reconstruct the four-dimensional information of an observed scene, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, a method using differential SAR tomography based on Khatri-Rao Subspace and Block Compressive Sensing (KRS-BCS) is proposed. Using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix, the proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao Subspace, and then the KRS-BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CS methods, the proposed KRS-BCS method not only maintains the high resolution characteristics of CS methods, but also has higher reconstruction accuracy and better performance. Simulations, ENVISAT-ASAR data and ground-based GPS data verify the effectiveness of the proposed method. -
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