Small-Sample Seabed Sediment Classification Based on Deep Learning

被引:3
|
作者
Zhao, Yuxin [1 ,2 ]
Zhu, Kexin [1 ,2 ]
Zhao, Ting [3 ]
Zheng, Liangfeng [1 ,2 ]
Deng, Xiong [1 ,2 ]
机构
[1] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China
[2] Minist Educ, Engn Res Ctr Nav Instruments, Harbin 150001, Peoples R China
[3] Harbin Engn Univ, Coll Underwater Acoust Engn, Harbin 150001, Peoples R China
关键词
acoustic remote sensing; seabed sediment classification; small-sample; side-scan sonar; self-attention generative adversarial network; self-attention densely connected convolutional network; SCAN SONAR IMAGES; RECOGNITION; MODEL;
D O I
10.3390/rs15082178
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming, which makes it crucial to design a well-performing classifier using small-sample seabed sediment data. To avoid data shortage, a self-attention generative adversarial network (SAGAN) was trained for data augmentation in this study. SAGAN consists of a generator, which generates data similar to the real image, and a discriminator, which distinguishes whether the image is real or generated. Furthermore, a new classifier for seabed sediment based on self-attention densely connected convolutional network (SADenseNet) is proposed to improve the classification accuracy of seabed sediment. The SADenseNet was trained using augmented images to improve the classification performance. The self-attention mechanism can scan the global image to obtain global features of the sediment image and is able to highlight key regions, improving the efficiency and accuracy of visual information processing. The proposed SADenseNet trained with the augmented dataset had the best performance, with classification accuracies of 92.31%, 95.72%, 97.85%, and 95.28% for rock, sand, mud, and overall, respectively, with a kappa coefficient of 0.934. The twelve classifiers trained with the augmented dataset improved the classification accuracy by 2.25%, 5.12%, 0.97%, and 2.64% for rock, sand, mud, and overall, respectively, and the kappa coefficient by 0.041 compared to the original dataset. In this study, SAGAN can enrich the features of the data, which makes the trained classification networks have better generalization. Compared with the state-of-the-art classifiers, the proposed SADenseNet has better classification performance.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Small-Sample Error Estimation for Bagged Classification Rules
    Vu, T. T.
    Braga-Neto, U. M.
    EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2010,
  • [22] Fads and fallacies in the name of small-sample microarray classification
    Braga-Neto, Ulisses
    IEEE SIGNAL PROCESSING MAGAZINE, 2007, 24 (01) : 91 - 99
  • [23] Small-Sample Error Estimation for Bagged Classification Rules
    T. T. Vu
    U. M. Braga-Neto
    EURASIP Journal on Advances in Signal Processing, 2010
  • [24] Coal Wettability Prediction Model Based on Small-Sample Machine Learning
    Wang, Jingyu
    Tang, Shuheng
    Zhang, Songhang
    Xi, Zhaodong
    Lv, Jianwei
    NATURAL RESOURCES RESEARCH, 2024, 33 (02) : 907 - 924
  • [25] Coal Wettability Prediction Model Based on Small-Sample Machine Learning
    Jingyu Wang
    Shuheng Tang
    Songhang Zhang
    Zhaodong Xi
    Jianwei Lv
    Natural Resources Research, 2024, 33 : 907 - 924
  • [26] Dynamic Attention Loss for Small-Sample Image Classification
    Cao, Jie
    Qiu, Yinping
    Chang, Dongliang
    Li, Xiaoxu
    Ma, Zhanyu
    2019 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2019, : 75 - 79
  • [27] Small-Sample Classification for Hyperspectral Images With EPF-Based Smooth Ordering
    Ye, Zhijing
    Zhang, Liming
    Zheng, Chengyong
    Peng, Jiangtao
    Benediktsson, Jon Atli
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62
  • [28] Small-sample continual learning classification method with vaccine to update memory cells based on the artificial immune system
    Zhang, Hongli
    Jiang, Lunchang
    Jiao, Wenhui
    Liu, Shulin
    Xiao, Haihua
    BIOSYSTEMS, 2022, 220
  • [29] Is cross-validation valid for small-sample microarray classification?
    Braga-Neto, UM
    Dougherty, ER
    BIOINFORMATICS, 2004, 20 (03) : 374 - 380
  • [30] A CNN-based self-supervised learning framework for small-sample near-infrared spectroscopy classification
    Zhao, Rongyue
    Li, Wangsen
    Xu, Jinchai
    Chen, Linjie
    Wei, Xuan
    Kong, Xiangzeng
    ANALYTICAL METHODS, 2025, 17 (05) : 1090 - 1100