Cross entropy of mass function and its application in similarity measure

Gao, Xiaozhuan; Pan, Lipeng; Deng, Yong

doi:10.1007/s10489-021-02890-6

Cited by 8 publications

(4 citation statements)

References 59 publications

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“…The experimental results show that the recognition accuracy of the method is significantly better than that with a single feature, especially when the SNR of the data is low (when the observation duration is 15 s, the accuracy of the method can still achieve 90%). This gives full play [24,29,31,34,56] with different sample frequencies.…”

Section: Discussionmentioning

confidence: 99%

“…We evaluate the performance of the proposed method with five classical baseline algorithms: the traditional DST [31], the Murphy method [34], the Gao method [56], the Zhang method [24], and the Zhou method [29]. The Murphy method mainly averages the BPAs generated by multiple features for target discrimination and fuses the average BPAs several times to obtain the final recognition result.…”

Section: Comparison With Other Baseline Methodsmentioning

confidence: 99%

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A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

Chen,

Zhang,

Zhang

et al. 2024

Remote Sensing

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Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison With Other Baseline Methodsmentioning

confidence: 99%

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

Chen,

Zhang,

Zhang

et al. 2024

Remote Sensing

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“…To further validate the effectiveness and novelty of the proposed method, a comparative analysis was conducted with other existing methods [38][39][40]. Among them, there are two traditional fusion recognition algorithms and a deep neural network method.…”

Section: Comparison Experimentsmentioning

confidence: 99%

“…Zhou's method uses feature fusion based on Bayesian decision theory to identify radar deception jamming signals and uses kernel density estimation to improve the fusion algorithm [38]. Gao proposed a similarity standard based on cross-entropy to modify the basic probability assignment of multiple features and then performed fusion recognition based on DS evidence theory [39]. Dual-channel long short-term memory (DC-LSTM) is a deep learning method that inputs space IR object grayscale into two LSTM channels to extract global and local features, respectively [40].…”

Section: Comparison Experimentsmentioning

confidence: 99%

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Zhang,

Rao,

et al. 2024

Remote Sensing

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Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targets and decoys. However, most existing methods ignore the fuzziness of multi-dimensional features, and their performance mainly depends on the accuracy of feature extraction, with certain limitations in handling uncertainty and noise. This article proposes a space IR dim target fusion recognition method, which is based on fuzzy comprehensive of spatio-temporal correlation. First, we obtained multi-dimensional IR features of the target through multi-time and multi-spectral detectors, then we established and calculated the adaptive fuzzy-membership function of the features. Next, we applied the entropy weight method to ascertain the objective fusion weights of each feature and computed the spatially fuzzified fusion judgments for the targets. Finally, the fuzzy comprehensive function was used to perform temporal recursive judgment, and the ultimate fusion recognition result was obtained by integrating the results of each temporal recursive judgment. The simulation and comparative experimental results indicate that the proposed method improved the accuracy and robustness of IR dim target recognition in complex environments. Under ideal conditions, it can achieve an accuracy of 88.0% and a recall of 97.5% for the real target. In addition, this article also analyzes the impact of fusion feature combinations, fusion frame counts, different feature extraction errors, and feature database size on recognition performance. The research in this article can enable space-based IR detection systems to make more accurate and stable decisions, promoting defense capabilities and ensuring space security.

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An Entropy-Based Computational Classifier for Positive and Negative Emotions in Voice Signals

Herrera-Ortiz

Yáñez-Casas²,

Hernández-Gómez³

et al. 2022

Communications in Computer and Information Science

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Cross entropy of mass function and its application in similarity measure

Cited by 8 publications

References 59 publications

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

An Entropy-Based Computational Classifier for Positive and Negative Emotions in Voice Signals

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