基于半正定約束的極化相似度最優(yōu)模型匹配目標(biāo)分解

范慶輝; 盧紅喜; 保錚; 肖春寶

doi:10.11999/JEIT141468

基于半正定約束的極化相似度最優(yōu)模型匹配目標(biāo)分解

doi: 10.11999/JEIT141468 cstr: 32379.14.JEIT141468

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61271024, 61201292, 61201283)，新世紀(jì)優(yōu)秀人才支持計(jì)劃(NCET-09-0630)，全國(guó)優(yōu)秀博士學(xué)位論文作者專(zhuān)項(xiàng)資金(FANEDD-201156)，省部級(jí)基金和中央高?；究蒲袠I(yè)務(wù)費(fèi)

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出版歷程
- 收稿日期: 2014-11-24
- 修回日期: 2015-04-24
- 刊出日期: 2015-08-19

Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity

摘要

摘要: 目標(biāo)分解是實(shí)現(xiàn)極化合成孔徑雷達(dá)目標(biāo)分類(lèi)、檢測(cè)與識(shí)別應(yīng)用的重要手段。傳統(tǒng)方法由于優(yōu)先對(duì)體散射分量進(jìn)行提取，其體散射能量的高估或二面角散射能量的低估現(xiàn)象較為嚴(yán)重。該文通過(guò)引入極化相似度量，基于數(shù)據(jù)驅(qū)動(dòng)自適應(yīng)地對(duì)基本散射機(jī)制的最優(yōu)匹配模型進(jìn)行選擇。在此基礎(chǔ)上，根據(jù)極化相似度量確定基本散射機(jī)制散射能量提取的優(yōu)先順序，并以各階次剩余矩陣能量非負(fù)為約束，最終確定面散射、二面角散射、體散射這3種基本散射機(jī)制的能量貢獻(xiàn)值。實(shí)測(cè)數(shù)據(jù)處理結(jié)果及其與光學(xué)圖像的對(duì)比結(jié)果表明，該文方法獲取的極化目標(biāo)分解結(jié)果優(yōu)于傳統(tǒng)方法，能夠準(zhǔn)確地提取目標(biāo)區(qū)域的基本散射特征。
- 極化合成孔徑雷達(dá) /
- 目標(biāo)分解 /
- 極化相似度 /
- 最優(yōu)模型匹配
Abstract: Target decomposition is an important tool to realize target classification, detection and recognition applications with Polarimetric SAR (PolSAR). However, the traditional method with priority of volume scattering component extraction seriously performs overestimation in the volume scattering energy or underestimation in the dihedral scattering energy. In this paper, by introducing polarimetric similarity measure, data-driven model- matching for basic scattering mechanism is proposed. On this basis, the priority of scattering mechanisms energy extraction is determined with the similarity measure. Based on the non-negative constraint of energy, all the orders of residual matrix are reextracted for the final energy contribution of the dihedral scattering, volume scattering, and surface scattering mechanism. The processing results of real data and their comparison with the optical image results show that the proposal is better than traditional methods for the accurate extracttion of the basic scattering characteristics in the targets region.
- Polarimetric SAR (PolSAR) /
- Target decomposition /
- Polarization similarity /
- Optimal model matching

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參考文獻(xiàn)(17)

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