自監(jiān)督解耦動(dòng)態(tài)分類器的小樣本類增量SAR圖像目標(biāo)識(shí)別

趙琰; 趙凌君; 張思乾; 計(jì)科峰; 匡綱要

doi:10.11999/JEIT231470

自監(jiān)督解耦動(dòng)態(tài)分類器的小樣本類增量SAR圖像目標(biāo)識(shí)別

doi: 10.11999/JEIT231470

國(guó)防科技大學(xué)電子科學(xué)學(xué)院長(zhǎng)沙 410073

詳細(xì)信息

作者簡(jiǎn)介:
趙琰：男，博士生，研究方向?yàn)镾AR圖像目標(biāo)識(shí)別、增量學(xué)習(xí)、小樣本學(xué)習(xí)

趙凌君：女，博士，副教授，研究方向?yàn)檫b感信息處理、SAR圖像目標(biāo)識(shí)別

張思乾：女，博士，副教授，研究方向?yàn)檫b感信息處理、SAR圖像目標(biāo)識(shí)別

計(jì)科峰：男，博士，教授，研究方向?yàn)镾AR目標(biāo)電磁散射特性建模、特征提取、檢測(cè)識(shí)別

匡綱要：男，博士，教授，研究方向?yàn)檫b感信息處理、SAR圖像目標(biāo)識(shí)別

通訊作者:
趙凌君　nudtzlj@163.com

中圖分類號(hào): TN957; TP751.2
計(jì)量
- 文章訪問數(shù): 318
- HTML全文瀏覽量: 341
- PDF下載量: 98
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-01-10
- 修回日期: 2024-06-17
- 網(wǎng)絡(luò)出版日期: 2024-06-24
- 刊出日期: 2024-10-30

Few-Shot Class-Incremental SAR Image Target Recognition using Self-supervised Decoupled Dynamic Classifier

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

摘要

摘要: 為提升基于深度學(xué)習(xí)(DL)的合成孔徑雷達(dá)自動(dòng)目標(biāo)識(shí)別(SAR ATR)系統(tǒng)在開放動(dòng)態(tài)的非合作場(chǎng)景中對(duì)新類別目標(biāo)的持續(xù)敏捷識(shí)別能力，該文研究了SAR ATR的小樣本類增量學(xué)習(xí)(FSCIL)問題，并提出了自監(jiān)督解耦動(dòng)態(tài)分類器(SDDC)。針對(duì)FSCIL 中“災(zāi)難性遺忘”和“過擬合”本質(zhì)難點(diǎn)和SAR ATR領(lǐng)域挑戰(zhàn)，根據(jù)SAR圖像目標(biāo)信息的部件化與方位角敏感性特點(diǎn)，于圖像域構(gòu)建了基于散射部件混淆與旋轉(zhuǎn)模塊(SCMR)的自監(jiān)督學(xué)習(xí)任務(wù)，以提升目標(biāo)表征的泛化性與穩(wěn)健性。同時(shí)，設(shè)計(jì)了類印記交叉熵(CI-CE)損失并以參數(shù)解耦學(xué)習(xí)(PDL)策略對(duì)模型動(dòng)態(tài)微調(diào)，以對(duì)新舊知識(shí)平衡判別。實(shí)驗(yàn)在由MSTAR和SAR-AIRcraft-1.0數(shù)據(jù)集分別構(gòu)建的覆蓋多種目標(biāo)類別、觀測(cè)條件和成像平臺(tái)的FSCIL場(chǎng)景上驗(yàn)證了該算法開放動(dòng)態(tài)環(huán)境的適應(yīng)能力。
- SAR目標(biāo)識(shí)別 /
- 小樣本類增量學(xué)習(xí) /
- 自監(jiān)督學(xué)習(xí) /
- 深度學(xué)習(xí)
Abstract: To power Deep-Learning (DL) based Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) systems with the capability of learning new-class targets incrementally and rapidly in openly dynamic non-cooperative situations, the problem of Few-Shot Class-Incremental Learning (FSCIL) of SAR ATR is researched and a Self-supervised Decoupled Dynamic Classifier (SDDC) is proposed. Considering solving both the intrinsic Catastrophic forgetting and Overfitting dilemma of the FSCIL and domain challenges of SAR ATR, a self-supervised learning task powered by Scattering Component Mixup and Rotation (SCMR) is designed to improve the model’s generalizability and stability for target representation, leveraged by the partiality and azimuth dependence of target information in SAR imagery. Meanwhile, a Class-Imprinting Cross-Entropy (CI-CE) and a Parameter Decoupled Learning (PDL) strategy are designed to fine-tune networks dynamically to identify old and new targets evenly. Experiments on various FSCIL scenarios constructed by the MSTAR and the SAR-AIRcraft-1.0 datasets covering diverse target categories, observing environments, and imaging payloads, verify the method’s adaptability to openly dynamic world.
- SAR target recognition /
- Few-Shot Class-Incremental Learning(FSCIL) /
- Self-supervised learning /
- Deep Learning(DL)

HTML全文

圖 1 自監(jiān)督動(dòng)態(tài)解耦分類器(SDDC)整體結(jié)構(gòu)

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圖 2 SAR圖像目標(biāo)結(jié)構(gòu)與高維語(yǔ)義信息的方位角敏感性與相位模糊性

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圖 3 基于誤差解耦理論對(duì)類印記初始化優(yōu)勢(shì)分析

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圖 4 FSCIL數(shù)據(jù)集構(gòu)建過程

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圖 5 MSTAR數(shù)據(jù)集10類目標(biāo)SAR圖像與光學(xué)圖像對(duì)比

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圖 6 SAR-AIRcarft-1.0數(shù)據(jù)集7類目標(biāo)SAR圖像與光學(xué)圖像對(duì)比

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圖 7 SMO對(duì)算法特征提取能力改善可視化結(jié)果

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圖 8 采用SMO前后算法所提目標(biāo)梯度激活圖與特征稀疏性與響應(yīng)值

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圖 9 采用SRO前后算法所提目標(biāo)方位角特征歸一化值與梯度激活圖

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圖 10 采用SRO前后算法所提類內(nèi)特征相似性均值與方差

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圖 11 算法分類準(zhǔn)確率與歸一化分類器值。

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圖 12 PDL不同解耦階段對(duì)算法在多種評(píng)價(jià)指標(biāo)上分類性能影響的柱狀圖

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圖 13 算法不同解耦位置對(duì)基類和增量類目標(biāo)的梯度激活圖

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圖 14 不同算法在MSTAR-FSCIL數(shù)據(jù)集和SAR-AIRcraft-1.0-FSCIL數(shù)據(jù)集測(cè)試性能曲線圖

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圖 15 對(duì)比基準(zhǔn)分類結(jié)果混淆矩陣

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圖 16 不同學(xué)習(xí)階段MSTAR-FSCIL數(shù)據(jù)集類特征空間t-SNE分布

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圖 17 不同學(xué)習(xí)階段SAR-AIRcraft-1.0-FSCIL數(shù)據(jù)集類特征空間t-SNE分布

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1 散射混淆操作(SMO)處理過程

輸入：小批次基類訓(xùn)練樣本 ${\boldsymbol} = \{ ({{\boldsymbol{x}}_i},{y_i})\} _{i = 1}^{\|{N_{}}\|} \in {{\boldsymbol{D}}^1}$
初始化：混淆次數(shù) R ，混淆樣本存儲(chǔ)列表${S_{{\text{smo}}}}$
For r = 1 to R do
步驟1　采樣隨機(jī)順序數(shù)據(jù)${\boldsymbol{\tilde b}} = \{ ({{\boldsymbol{\tilde x}}_i},{\tilde y_i})\} _{i = 1}^{\|{N_{}}\|}$
步驟2　獲取原始$\{ {y_i}\} _{i = 1}^{\|{N_{}}\|}$與隨機(jī)順序$\{ {\tilde y_i}\} _{i = 1}^{\|{N_{}}\|}$標(biāo)簽
步驟3　從${\boldsymbol}$中選取${\boldsymbol}' = \{ ({{\boldsymbol{x}}'_i},{y'_i})\} $，從${\boldsymbol{\tilde b}}$中選取　　　　　 $ {\boldsymbol{\tilde b}}' = \{ ({{\boldsymbol{\tilde x}}'_i},{\tilde y'_i})\} $，滿足${y'_j} \ne {\tilde y'_j},\forall j \in \|{N_{}}\|$
步驟4　根據(jù)式(1)從${\boldsymbol}'$與 $ {\boldsymbol{\tilde b}}' $中生成散射混淆樣本集　　　　　 $ {{\stackrel \frown{{\boldsymbol}} }} = \{ ({{{\stackrel \frown{{\boldsymbol{x}}} }}_i},{\stackrel \frown{y} _i})\} $
步驟5　將$ {{\stackrel \frown{{\boldsymbol}} }} $添加至${S_{{\text{smo}}}}$
End for
輸出：${S_{{\text{smo}}}}$

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表 1 MSTAR-FSCIL數(shù)據(jù)集配置

學(xué)習(xí)階段	序號(hào)	類別	型號(hào)	訓(xùn)練集	測(cè)試集
基類	1	BTR70	c71	233	196
	2	2S1	b01	299	274
	3	BRDM2	E-71	298	274
	4	BMP2	9563	233	196
增量類	5	ZIL131	E12	5	274
	6	T62	A51	5	274
	7	D7	13015	5	274
	8	BTR60	7532	5	195
	9	T72	132	5	196
	10	ZSU234	d08	5	274

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表 2 SAR-AIRcraft-1.0-FSCIL數(shù)據(jù)集配置表

學(xué)習(xí)階段	序號(hào)	類別	訓(xùn)練集	測(cè)試集
基類	1	Other	2 000	200
	2	A220	2 000	200
	3	Boeing787	2 000	200
	4	Boeing737	2 000	200
增量類	5	A320	5	200
	6	ARJ21	5	200
	7	A330	5	200

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表 3 SDDC算法模塊與損失貢獻(xiàn)(%)

遷移性		判別性		Avg.Acc	Acc.Old	Acc.New	PD
SMO	SRO	CI-CE	PDL	Avg.Acc	Acc.Old	Acc.New	PD
				75.26	78.87	49.02	41.50
√				78.35	81.41	54.16	37.77
	√			77.79	81.27	52.24	35.29
√	√			79.50	82.68	54.46	34.47
√	√	√		73.30	72.62	79.12	55.83
√	√		√	75.81	79.01	47.22	42.35
√	√	√	√	81.73	83.37	66.70	31.61

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