Hao Tianduo, Cui Chen, Gong Yang, Sun Congyi. Waveform Design for Cognitive Radar Under Low PAR Constraints by Convex Optimization[J]. Journal of Radars, 2018, 7(4): 498-506. doi: 10.12000/JR18002
Citation: YU Yue, WANG Chen, SHI Jun, et al. Modeling and correction of label noise uncertainty for SAR ATR[J]. Journal of Radars, 2024, 13(5): 974–984. doi: 10.12000/JR24130

Modeling and Correction of Label Noise Uncertainty for SAR ATR

DOI: 10.12000/JR24130 CSTR: 32380.14.JR24130
Funds:  The National Natural Science Foundation of China (62201375), Natural Science Foundation of Jiangsu Province (BK20220635), Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1762), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300756)
More Information
  • The success of deep supervised learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) relies on a large number of labeled samples. However, label noise often exists in large-scale datasets, which highly influence network training. This study proposes loss curve fitting-based label noise uncertainty modeling and a noise uncertainty-based correction method. The loss curve is a discriminative feature to model label noise uncertainty using an unsupervised fuzzy clustering algorithm. Then, according to this uncertainty, the sample set is divided into different subsets: the noisy-label set, clean-label set, and fuzzy-label set, which are further used in training loss with different weights to correct label noise. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove that our method can deal with varying ratios of label noise during network training and correct label noise effectively. When the training dataset contains a small ratio of label noise (40%), the proposed method corrects 98.6% of these labels and trains the network with 98.7% classification accuracy. Even when the proportion of label noise is large (80%), the proposed method corrects 87.8% of label noise and trains the network with 82.3% classification accuracy.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 14.9 %其他: 14.9 %其他: 0.1 %其他: 0.1 %China: 0.8 %China: 0.8 %India: 0.1 %India: 0.1 %United States: 0.0 %United States: 0.0 %[]: 0.3 %[]: 0.3 %三明: 0.0 %三明: 0.0 %上海: 1.3 %上海: 1.3 %东京: 0.0 %东京: 0.0 %东莞: 0.0 %东莞: 0.0 %中卫: 0.1 %中卫: 0.1 %临沂: 0.0 %临沂: 0.0 %乌海: 0.0 %乌海: 0.0 %亳州: 0.0 %亳州: 0.0 %佛山: 0.1 %佛山: 0.1 %佳木斯: 0.0 %佳木斯: 0.0 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.0 %兰辛: 0.0 %加利福尼亚州: 0.1 %加利福尼亚州: 0.1 %包头: 0.0 %包头: 0.0 %北京: 17.3 %北京: 17.3 %北京市: 0.2 %北京市: 0.2 %北海: 0.0 %北海: 0.0 %十堰: 0.1 %十堰: 0.1 %南京: 0.4 %南京: 0.4 %南宁: 0.1 %南宁: 0.1 %南昌: 0.0 %南昌: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.0 %台北: 0.0 %台州: 0.1 %台州: 0.1 %合肥: 0.5 %合肥: 0.5 %呼和浩特: 0.1 %呼和浩特: 0.1 %哥伦布: 0.0 %哥伦布: 0.0 %嘉兴: 0.1 %嘉兴: 0.1 %圣地亚哥: 0.0 %圣地亚哥: 0.0 %大连: 0.0 %大连: 0.0 %天津: 0.4 %天津: 0.4 %太原: 0.0 %太原: 0.0 %宁波: 0.1 %宁波: 0.1 %安康: 0.1 %安康: 0.1 %安阳: 0.2 %安阳: 0.2 %宣城: 0.1 %宣城: 0.1 %巴中: 0.0 %巴中: 0.0 %常州: 0.1 %常州: 0.1 %广州: 0.3 %广州: 0.3 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %张家口: 1.2 %张家口: 1.2 %张家口市: 0.1 %张家口市: 0.1 %怒江: 0.0 %怒江: 0.0 %成都: 0.3 %成都: 0.3 %扬州: 0.1 %扬州: 0.1 %新乡: 0.4 %新乡: 0.4 %无锡: 0.1 %无锡: 0.1 %旧金山: 0.0 %旧金山: 0.0 %昆明: 0.0 %昆明: 0.0 %昌吉: 0.0 %昌吉: 0.0 %朝阳: 0.0 %朝阳: 0.0 %杭州: 1.3 %杭州: 1.3 %格兰特县: 0.1 %格兰特县: 0.1 %武汉: 0.2 %武汉: 0.2 %沈阳: 0.1 %沈阳: 0.1 %沧州: 0.0 %沧州: 0.0 %洛阳: 0.0 %洛阳: 0.0 %济南: 0.1 %济南: 0.1 %淄博: 0.0 %淄博: 0.0 %淮南: 0.0 %淮南: 0.0 %深圳: 0.4 %深圳: 0.4 %湖州: 0.0 %湖州: 0.0 %湘潭: 0.0 %湘潭: 0.0 %滨州: 0.0 %滨州: 0.0 %漯河: 0.2 %漯河: 0.2 %潍坊: 0.0 %潍坊: 0.0 %烟台: 0.0 %烟台: 0.0 %玉林: 0.1 %玉林: 0.1 %珠海: 0.0 %珠海: 0.0 %白城: 0.0 %白城: 0.0 %白银: 0.3 %白银: 0.3 %盐城: 0.1 %盐城: 0.1 %石家庄: 0.3 %石家庄: 0.3 %福州: 0.1 %福州: 0.1 %秦皇岛: 0.1 %秦皇岛: 0.1 %纽约: 0.1 %纽约: 0.1 %美国伊利诺斯芝加哥: 0.0 %美国伊利诺斯芝加哥: 0.0 %芒廷维尤: 12.6 %芒廷维尤: 12.6 %芝加哥: 0.2 %芝加哥: 0.2 %苏州: 0.1 %苏州: 0.1 %衡水: 0.0 %衡水: 0.0 %衡阳: 0.1 %衡阳: 0.1 %衢州: 0.0 %衢州: 0.0 %西宁: 37.8 %西宁: 37.8 %西安: 0.4 %西安: 0.4 %贵港: 0.2 %贵港: 0.2 %赤峰: 0.0 %赤峰: 0.0 %运城: 0.1 %运城: 0.1 %连云港: 0.0 %连云港: 0.0 %邯郸: 0.1 %邯郸: 0.1 %郑州: 1.3 %郑州: 1.3 %鄂州: 0.1 %鄂州: 0.1 %重庆: 0.1 %重庆: 0.1 %银川: 0.1 %银川: 0.1 %镇江: 0.1 %镇江: 0.1 %长春: 0.0 %长春: 0.0 %长沙: 0.7 %长沙: 0.7 %长治: 0.0 %长治: 0.0 %防城港: 0.0 %防城港: 0.0 %青岛: 0.4 %青岛: 0.4 %鞍山: 0.0 %鞍山: 0.0 %黄冈: 0.1 %黄冈: 0.1 %龙岩: 0.0 %龙岩: 0.0 %其他其他ChinaIndiaUnited States[]三明上海东京东莞中卫临沂乌海亳州佛山佳木斯兰州兰辛加利福尼亚州包头北京北京市北海十堰南京南宁南昌厦门台北台州合肥呼和浩特哥伦布嘉兴圣地亚哥大连天津太原宁波安康安阳宣城巴中常州广州库比蒂诺张家口张家口市怒江成都扬州新乡无锡旧金山昆明昌吉朝阳杭州格兰特县武汉沈阳沧州洛阳济南淄博淮南深圳湖州湘潭滨州漯河潍坊烟台玉林珠海白城白银盐城石家庄福州秦皇岛纽约美国伊利诺斯芝加哥芒廷维尤芝加哥苏州衡水衡阳衢州西宁西安贵港赤峰运城连云港邯郸郑州鄂州重庆银川镇江长春长沙长治防城港青岛鞍山黄冈龙岩

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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