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  • Open Access


    A Multi Moving Target Recognition Algorithm Based on Remote Sensing Video

    Huanhuan Zheng1,*, Yuxiu Bai1, Yurun Tian2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 585-597, 2023, DOI:10.32604/cmes.2022.020995

    Abstract The Earth observation remote sensing images can display ground activities and status intuitively, which plays an important role in civil and military fields. However, the information obtained from the research only from the perspective of images is limited, so in this paper we conduct research from the perspective of video. At present, the main problems faced when using a computer to identify remote sensing images are: They are difficult to build a fixed regular model of the target due to their weak moving regularity. Additionally, the number of pixels occupied by the target is not enough for accurate detection. However,… More >

  • Open Access


    Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

    Liguo Zhang1,2, Zilin Tian1, Yan Zhang3,*, Tong Shuai4, Shuo Liang4, Zhuofei Wu5

    Journal of New Media, Vol.4, No.2, pp. 59-71, 2022, DOI:10.32604/jnm.2022.029360

    Abstract In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just a small number of training data are available. In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition, in this paper, we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model (RBnet) for SAR image target recognition. In the RBnet, a… More >

  • Open Access


    Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

    Linshan Shen1, Ye Tian1,*, Liguo Zhang1,2, Guisheng Yin1, Tong Shuai3, Shuo Liang3, Zhuofei Wu4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 465-476, 2022, DOI:10.32604/cmc.2022.027488

    Abstract The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data,… More >

  • Open Access


    Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

    Yuheng Sun1, Ye Mu1, 2, 3, 4, *, Qin Feng5, Tianli Hu1, 2, 3, 4, He Gong1, 2, 3, 4, Shijun Li1, 2, 3, 4, Jing Zhou6

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1317-1328, 2020, DOI:10.32604/cmc.2020.010510

    Abstract In large-scale deer farming image analysis, K-means or maximum betweenclass variance (Otsu) algorithms can be used to distinguish the deer from the background. However, in an actual breeding environment, the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer. Also, when the target and background grey values are similar, the multiple background targets cannot be completely separated. To better identify the posture and behaviour of deer in a deer shed, we used digital image processing to separate the deer from the background. To address the problems… More >

  • Open Access


    ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network

    Nianbin Wang1, Ming He1,2, Jianguo Sun1,*, Hongbin Wang1, Lianke Zhou1, Ci Chu1, Lei Chen3

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 169-181, 2019, DOI:10.32604/cmc.2019.03709

    Abstract Underwater target recognition is a key technology for underwater acoustic countermeasure. How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals. In this paper, the deep learning model is applied to underwater target recognition. Improved anti-noise Power-Normalized Cepstral Coefficients (ia-PNCC) is proposed, based on PNCC applied to underwater noises. Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity. The method is combined with a convolutional neural network in order to recognize the underwater target. Experiment results show that the… More >

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