小樣本SAR目標(biāo)的雙重一致性因果識別方法

王陳煒; 羅思懿; 黃鈺林; 裴季方; 張寅; 楊建宇

doi:10.11999/JEIT240140

小樣本SAR目標(biāo)的雙重一致性因果識別方法

doi: 10.11999/JEIT240140

電子科技大學(xué)信息與通信工程學(xué)院成都 611731

基金項目: 四川省自然科學(xué)基金 (2023NSFSC1970)

詳細信息

作者簡介:
王陳煒：男，博士，研究方向為雷達目標(biāo)識別、機器學(xué)習(xí)、雷達探測成像等

羅思懿：女，碩士，研究方向為雷達目標(biāo)識別、機器學(xué)習(xí)、雷達探測成像等

黃鈺林：男，教授，研究方向為雷達探測成像、雷達目標(biāo)檢測與識別、人工智能與機器學(xué)習(xí)等

裴季方：男，副研究員，研究方向為雷達目標(biāo)識別、機器學(xué)習(xí)、雷達探測成像等

張寅：男，研究員，研究方向為新體制雷達、雷達信號處理、雷達成像等

楊建宇：男，教授，研究方向為雷達探測與成像、信號檢測與估計等

通訊作者:
黃鈺林　yulinhuang@uestc.edu.cn

中圖分類號: TN958
計量
- 文章訪問數(shù): 265
- HTML全文瀏覽量: 105
- PDF下載量: 50
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-03-06
- 修回日期: 2024-08-28
- 網(wǎng)絡(luò)出版日期: 2024-09-01
- 刊出日期: 2024-10-30

A Causal Interventional SAR ATR Method with Limited Data via Dual Consistency

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The Natural Science Foundation of Sichuan Province (2023NSFSC1970)

摘要

摘要: 在小樣本條件下提升方法的泛化性能，是合成孔徑雷達自動目標(biāo)識別(SAR ATR)的重要研究方向。針對該方向中的基礎(chǔ)理論問題，該文建立了一個SAR ATR因果模型，證明了SAR圖像中背景、相干斑等干擾在充足樣本條件下可以被忽略；但在小樣本條件下，這些因素將成為識別中的混雜因子，在提取的SAR圖像特征中引入虛假相關(guān)性，影響SAR ATR性能。為了甄別和消除這些特征中的虛假效應(yīng)，該文提出一個基于雙重一致性的小樣本SAR ATR方法，其中雙重一致性包括類內(nèi)一致性掩碼和效應(yīng)一致性損失。首先，基于鑒別特征應(yīng)具有類內(nèi)一致和類間差異的原則，利用類內(nèi)一致性掩碼，捕獲目標(biāo)的類內(nèi)一致鑒別特征，甄別出目標(biāo)特征中的混淆部分，準(zhǔn)確估計出干擾引入的虛假效應(yīng)。其次，基于不變風(fēng)險最小化的思想，利用效應(yīng)一致性損失，將經(jīng)驗風(fēng)險最小化數(shù)據(jù)量需求轉(zhuǎn)變?yōu)閷π?yīng)相似度的度量需求，降低虛假效應(yīng)消除對數(shù)據(jù)量的需求，消除特征中的虛假效應(yīng)。因而，所提基于雙重一致性的小樣本SAR ATR方法可實現(xiàn)特征提取中的真實因果，實現(xiàn)準(zhǔn)確的識別性能。兩個基準(zhǔn)數(shù)據(jù)集上的識別實驗，驗證了該方法的合理性和有效性，可提升小樣本條件下SAR目標(biāo)識別的性能。
- 合成孔徑雷達 /
- 自動目標(biāo)識別 /
- 小樣本 /
- 因果推斷
Abstract: Improving the generalization performance of methods under limited sample conditions is an important research direction in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR). Addressing the fundamental problem in this field, a causal model is established in this paper for SAR ATR, demonstrating that interferences in SAR images, such as background and speckle, can be neglected under sufficient sample conditions. However, under limited sample conditions, these factors become confounding variables, introducing spurious correlations into the extracted SAR image features and affecting the generalization of SAR ATR. To accurately identify and eliminate these spurious effects in the features, this paper proposes a limited-sample SAR ATR method via dual consistency, which includes an intra-class feature consistency mask and effect-consistency loss. Firstly, based on the principle that discriminative features should have intra-class consistency and inter-class differences, the intra-class feature consistency mask is used to capture the consistent discriminative features of the target, subtracting the confounded part in the target features, and identifying the spurious effects introduced by interferences. Secondly, based on the idea of invariant risk minimization, the effect-consistency loss transforms the data requirement of empirical risk minimization into a need for labeling the similarity among effects of different samples, reducing the data demand for eliminating spurious effects and removing the spurious effects in the features. Thus, the limited-sample SAR ATR method proposed in this paper achieves true causal feature extraction and accurate recognition performance. Experiments on two benchmark datasets validate the effectiveness of this method which can achieve superior performance of SAR target recognition with limited sample.
- Synthetic Aperture Radar (SAR) /
- Automatic target recognition /
- Limited data /
- Causal inference

HTML全文

圖 1 有限SAR樣本在ATR中引入的因果效應(yīng)變化

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圖 2 本文方法的總體框架

下載: 全尺寸圖片幻燈片

表 1 MSTAR數(shù)據(jù)集下的訓(xùn)練集與測試集

類別	訓(xùn)練集		測試集
類別	數(shù)量	俯仰角	數(shù)量	俯仰角
BMP2-9563	233	17°	195	15°
BRDM2-E71	298		274
BTR60-7532	256		195
BTR70-c71	233		196
D7-92	299		274
2S1-b01	299		274
T62-A51	299		273
T72-132	232		196
ZIL131-E12	299		274
ZSU234-d08	299		274

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表 2 MSTAR數(shù)據(jù)集不同訓(xùn)練樣本數(shù)量下10類目標(biāo)的識別性能(%)

類別	每類目標(biāo)訓(xùn)練樣本數(shù)
類別	5	10	20	25	30	40	60	80	100
BMP2	73.33	83.59	92.31	94.87	95.90	97.95	98.97	97.44	98.46
BRDM2	77.74	94.89	93.80	96.72	97.08	98.18	99.27	99.64	98.91
BTR60	83.59	86.67	91.79	94.36	94.87	95.90	98.97	96.92	97.44
BTR70	70.92	90.82	92.35	95.92	96.94	98.47	97.96	98.98	98.98
D7	68.98	83.58	92.70	95.26	95.99	97.45	98.54	99.64	99.64
2S1	81.75	80.29	93.80	95.99	97.08	98.54	98.91	99.64	98.91
T62	74.36	90.11	92.67	95.60	97.07	98.53	97.44	99.63	99.63
T72	89.80	86.22	91.84	95.41	96.94	98.47	98.98	98.98	98.98
ZIL131	68.25	85.77	93.07	96.72	97.81	98.54	98.91	99.64	99.64
ZSU234	74.45	80.29	93.80	96.72	97.08	98.18	98.54	99.64	99.64
平均值	76.13	86.38	93.00	96.07	97.01	98.24	98.79	99.32	99.35

下載: 導(dǎo)出CSV

表 3 OpenSARShip數(shù)據(jù)集中訓(xùn)練與測試集

類別	成像條件	訓(xùn)練樣本數(shù)	測試樣本數(shù)	總樣本數(shù)
Bulk Carrier	VH 和 VV, C 波段分辨率=5～20 m 入射角=20°～45° 仰角=±11° Rg20 m×az22 m	200	475	675
Container Ship		200	811	1011
Tanker		200	354	554

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表 4 OpenSARShip數(shù)據(jù)集不同訓(xùn)練樣本數(shù)量下3類艦船目標(biāo)的識別性能(%)

類別	每類訓(xùn)練樣本數(shù)
類別	10	20	30	40	50	60	70	80	100	200
Bulk Carrier	65.89	63.58	65.68	75.16	60.21	66.11	68.21	65.26	72.84	73.47
Container Ship	71.27	75.46	75.96	76.57	82.49	79.04	80.02	85.20	83.35	90.75
Tanker	79.10	82.77	81.92	74.01	84.46	86.16	84.46	81.92	78.81	82.77
平均值	71.51	73.75	74.41	75.66	76.58	76.92	77.66	78.86	79.41	84.12

下載: 導(dǎo)出CSV

表 5 消融實驗：20個訓(xùn)練樣本下不同消融配置的識別性能(%)

方法配置	特征	效應(yīng)	BMP2	BRDM2	BTR60	BTR70	D7	2S1	T62	T72	ZIL131	ZSU234	平均值
V1	×	×	80.51	75.91	74.87	80.10	73.72	76.28	78.75	82.14	84.31	86.86	79.59
V2	√	×	88.21	89.05	81.54	88.27	85.77	96.35	81.68	84.18	87.23	94.16	88.16
V3	√	√	92.31	93.80	91.79	92.35	92.70	93.80	92.67	91.84	93.07	93.80	93.00

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表 6 MSTAR數(shù)據(jù)集下SAR目標(biāo)識別性能對比(%)

方法		每類樣本數(shù)
方法		10	20	40	55	80	110	165	220	All data
傳統(tǒng)方法	PCA+SVM ^[14]	–	76.43	87.95	–	92.48	–	–	–	94.32
	ADaboost ^[14]	–	75.68	86.45	–	91.45	–	–	–	93.51
	DGM^[14]	–	81.11	88.14	–	92.85	–	–	–	96.07
數(shù)據(jù)增強	GAN-CNN ^[14]	–	81.80	88.35	–	93.88	–	–	–	97.03
數(shù)據(jù)增強	MGAN-CNN^[14]	–	85.23	90.82	–	94.91	–	–	–	97.81
新穎模型	Deep CNN ^[15]	–	77.86	86.98	–	93.04	–	–	–	95.54
	Simple CNN ^[16]	–	75.88	–	–	–	–	–	–	–
	Dens-CapsNet ^[17]	80.26	92.95	96.50	–	–	–	–	–	99.75
	ASC-MACN ^[18]	62.85	79.46	–	–	–	–	–	–	99.42
	本文方法	86.38	93.00	98.24	–	99.32	–	–	–	–

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