面向SAR目標(biāo)識(shí)別成像參數(shù)敏感性的深度學(xué)習(xí)技術(shù)研究進(jìn)展
doi: 10.11999/JEIT240155
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國(guó)防科技大學(xué)電子科學(xué)學(xué)院電子信息系統(tǒng)復(fù)雜電磁環(huán)境效應(yīng)實(shí)驗(yàn)室 長(zhǎng)沙 410073
Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition
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Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
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摘要: 隨著人工智能技術(shù)的發(fā)展，基于深度神經(jīng)網(wǎng)絡(luò)的合成孔徑雷達(dá)(SAR)目標(biāo)識(shí)別得到了廣泛關(guān)注。然而，SAR系統(tǒng)的成像機(jī)制導(dǎo)致了圖像特性與成像參數(shù)之間的強(qiáng)相關(guān)性，因此深度學(xué)習(xí)框架下的目標(biāo)識(shí)別算法精度極易受成像參數(shù)敏感性的干擾，這成為了制約先進(jìn)智能算法部署到實(shí)際工程中的一大障礙。該文首先回顧了SAR圖像目標(biāo)識(shí)別技術(shù)的發(fā)展與相關(guān)數(shù)據(jù)集，從雷達(dá)工作的成像幾何、載荷參數(shù)和噪聲干擾3個(gè)角度，深入分析了成像參數(shù)變化對(duì)圖像特性的影響；然后，從模型、數(shù)據(jù)、特征3個(gè)維度，總結(jié)歸納了現(xiàn)有文獻(xiàn)關(guān)于深度學(xué)習(xí)技術(shù)對(duì)成像參數(shù)敏感性的魯棒性與泛化性這一問(wèn)題的研究進(jìn)展；接下來(lái)，匯總并分析了典型方法的實(shí)驗(yàn)結(jié)果；最后討論了在未來(lái)有望突破成像參數(shù)敏感性這一問(wèn)題的深度學(xué)習(xí)技術(shù)研究方向。
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關(guān)鍵詞:
- 合成孔徑雷達(dá) /
- 自動(dòng)目標(biāo)識(shí)別 /
- 深度學(xué)習(xí) /
- 域自適應(yīng) /
- 參數(shù)敏感性
Abstract: With the development of artificial intelligence technology, Synthetic Aperture Radar (SAR) target recognition based on deep neural networks has received widespread attention. However, the imaging mechanism of SAR system leads to a strong correlation between image characteristics and imaging parameters, so the algorithm accuracy under deep learning is easily disturbed by the sensitivity of imaging parameters, which becomes a major obstacle restricting the deployment of advanced intelligent algorithms to practical engineering applications. Firstly, in this paper, the developments of SAR image target recognition technology and related data sets are reviewed, and the influence of imaging parameters on image characteristics is analyzed deeply from three aspects, i.e., imaging geometry, radar parameter and noise interference. Then, the existing literature on the robustness and generalization of deep learning technology to imaging parameter sensitivity is summarized from the three dimensions of model, data and features. Thereafter, the experimental results of typical methods are summarized and analyzed. Finally, the research direction of deep learning technology which is expected to break through the sensitivity of imaging parameters in the future is discussed. -
表 1 SAR目標(biāo)識(shí)別開(kāi)源數(shù)據(jù)集
來(lái)源 數(shù)據(jù)集 目標(biāo)類(lèi)型 采集\仿真平臺(tái) 主要成像參數(shù)特點(diǎn) 實(shí)測(cè) MSTAR[7] 軍用車(chē)輛 機(jī)載SAR (1) X波段，HH極化，帶寬561 MHz，分辨率0.3 m
(2) 覆蓋15°,17°,30°和45° 4個(gè)俯仰角，0°～360°方位角(部分嚴(yán)重散焦圖像被剔除)Gotcha[18,19] 民用車(chē)輛 機(jī)載SAR (1) X波段，全極化，帶寬640 MHz
(2) 均勻覆蓋43.7°～45°中8個(gè)俯仰角，0°～360°方位角CircularSAR[20] 軍用車(chē)輛 機(jī)載SAR (1) X波段，帶寬 1800 MHz，分辨率0.1 m
(2) 覆蓋15°,26°,31°和45° 4個(gè)俯仰角，0°～360°方位角(部分嚴(yán)重散焦圖像被剔除)SAR-ACD[21] 民用飛機(jī) GF3 C波段，HH極化，分辨率1 m OpenSARShip-1.0/2.0[22,23] 民用艦船 Sential-1 C波段，VV和VH極化，分辨率20～22 m FuSAR-Ship[24] 民用艦船 GF3 C波段，HH和VV極化，分辨率1.5 m 仿真 SarSIM[25] 民用車(chē)輛 CST軟件 (1) X波段，HH極化，分辨率0.3 m, 3種地面環(huán)境
(2) 覆蓋15°,17°,25°,30°,35°,40°和45° 7個(gè)俯仰角，0°～360°方位角(5°為間隔)SAMPLE[26] 軍用車(chē)輛 XPatch軟件 (1) X波段，HH極化，帶寬561 MHz，分辨率0.3 m
(2) 覆蓋15°～17°俯仰角，10°～80°方位角
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表 2 優(yōu)缺點(diǎn)及代表性方法特點(diǎn)總結(jié)
技術(shù)類(lèi)型 優(yōu)缺點(diǎn) 代表性參考文獻(xiàn) 主要特點(diǎn) 模型端 (1) 提升融合后特征的物理可解釋性
(2) 傳統(tǒng)/物理特征的魯棒性仍有提升空間文獻(xiàn)[27] 將CNN模型與電磁散射特征融合 文獻(xiàn)[28] 將CNN模型與傳統(tǒng)幾何特征融合 數(shù)據(jù)端 (1) 僅需在數(shù)據(jù)端操作，易于工程實(shí)現(xiàn)
(2) 性能受到擴(kuò)增部分?jǐn)?shù)據(jù)的質(zhì)量影響文獻(xiàn)[29] 使用仿射變化、圖像旋轉(zhuǎn)擴(kuò)增訓(xùn)練集 文獻(xiàn)[30] 使用生成對(duì)抗網(wǎng)絡(luò)擴(kuò)增訓(xùn)練集 文獻(xiàn)[31] 使用電磁仿真數(shù)據(jù)擴(kuò)增訓(xùn)練集 特征端 (1) 泛化性提升顯著，存在理論基礎(chǔ)
(2) 直推式學(xué)習(xí)限制實(shí)際應(yīng)用場(chǎng)景文獻(xiàn)[32] 在特征層上對(duì)齊分布 文獻(xiàn)[33] 在特征層+像素層上對(duì)齊分布 文獻(xiàn)[34] 在特征層+像素層+決策層上對(duì)齊分布
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表 4 機(jī)載SAR車(chē)輛目標(biāo)數(shù)據(jù)集的成像參數(shù)
參數(shù) FARAD Ka FARAD X miniSAR 成像地點(diǎn) 美國(guó)科特蘭空軍基地 美國(guó)新墨西哥州 美國(guó)新墨西哥州 成像時(shí)間 2015.08 2015.10 2005.05 波段 Ka X Ku 中心頻率(GHz) 35.6 9.6 16.8 帶寬(GHz) 5 3 3 俯仰角度(°) 26～34 26～34 26～29 分辨率(m) 0.1 0.1 0.1 最大觀(guān)測(cè)距離(km) 6 12 8
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表 5 艦船檢測(cè)數(shù)據(jù)集中的四種星載SAR成像參數(shù)
參數(shù) Gaofen-3 TerraSAR-X Radarsat-2 Sentinel-1 軌道高度(km) 755 514 798 693 入射角度(°) 10～60 20～55 20～45 10～60 波段 C X C C 帶寬(MHz) 240 150 100 100 分辨率(m) 0.5～100 1～16 1～100 5～20 成像范圍(km) 10～650 5～100 20～50 20～400 俯仰掃描角度(°) ±20 ±25 ±11 ±20
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表 6 SOC與EOC條件中俯仰角變化情況
俯仰角(°) 類(lèi)別數(shù)量 訓(xùn)練數(shù)據(jù) 測(cè)試數(shù)據(jù) SOC(17°～15°) 17 15 10 EOC(17°～30°) 17 30 3 EOC(17°～45°) 17 45 3
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