Exploiting Superpixel-Based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification

被引:1
作者
Tang, Jiechen [1 ]
Tong, Hengjian [1 ]
Tong, Fei [2 ]
Zhang, Yun [2 ]
Chen, Weitao [1 ]
机构
[1] China Univ Geosci, Sch Comp Sci, 68 Jincheng St, East Lake New Technol Dev Zone, Wuhan 430078, Peoples R China
[2] Univ New Brunswick, Dept Geodesy & Geomatics Engn, 15 Dineen Dr, Fredericton, NB E3B 5A3, Canada
基金
中国国家自然科学基金;
关键词
high spatial resolution image; superpixel-based image classification; active learning; supervised learning; label spread; SEGMENTATION;
D O I
10.3390/rs15030715
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Superpixel-based classification using Active Learning (AL) has shown great potential in high spatial resolution remote sensing image classification tasks. However, in existing superpixel-based classification models using AL, the expert labeling information is only used on the selected informative superpixel while its neighboring superpixels are ignored. Actually, as most superpixels are over-segmented, a ground object always contains multiple superpixels. Thus, the center superpixel tends to have the same label as its neighboring superpixels. In this paper, to make full use of the expert labeling information, a Similar Neighboring Superpixels Search and Labeling (SNSSL) method was proposed and used in the AL process. Firstly, we identify superpixels with certain categories and uncertain superpixels by supervised learning. Secondly, we use the active learning method to process those uncertain superpixels. In each round of AL, the expert labeling information is not only used to enrich the training set but also used to label the similar neighboring superpixels. Similar neighboring superpixels are determined by computing the similarity of two superpixels according to CIELAB Dominant Colors distance, Correlation distance, Angular Second Moment distance and Contrast distance. The final classification map is composed of the supervised learning classification map and the active learning with SNSSL classification map. To demonstrate the performance of the proposed SNSSL method, the experiments were conducted on images from two benchmark high spatial resolution remote sensing datasets. The experiment shows that overall accuracy, average accuracy and kappa coefficients of the classification using the SNSSL have been improved obviously compared with the classification without the SNSSL.
引用
收藏
页数:17
相关论文
共 42 条
  • [1] SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
    Achanta, Radhakrishna
    Shaji, Appu
    Smith, Kevin
    Lucchi, Aurelien
    Fua, Pascal
    Suesstrunk, Sabine
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2012, 34 (11) : 2274 - 2281
  • [2] Aggarwal CC, 2014, CH CRC DATA MIN KNOW, P457
  • [3] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [4] XGBoost: A Scalable Tree Boosting System
    Chen, Tianqi
    Guestrin, Carlos
    [J]. KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 785 - 794
  • [5] Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation
    Chen, Yangyang
    Ming, Dongping
    Lv, Xianwei
    [J]. EARTH SCIENCE INFORMATICS, 2019, 12 (03) : 341 - 363
  • [6] A novel method for assessing the segmentation quality of high-spatial resolution remote-sensing images
    Cheng, Jiehai
    Bo, Yanchen
    Zhu, Yuxin
    Ji, Xiaole
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (10) : 3816 - 3839
  • [7] A study of efficiency and accuracy in the transformation from RGB to CIELAB color space
    Connolly, C
    Fliess, T
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 1997, 6 (07) : 1046 - 1048
  • [8] Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
    Csillik, Ovidiu
    [J]. REMOTE SENSING, 2017, 9 (03)
  • [9] Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images
    Demir, Begum
    Persello, Claudio
    Bruzzone, Lorenzo
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (03): : 1014 - 1031
  • [10] Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images
    Diao, Qi
    Dai, Yaping
    Zhang, Ce
    Wu, Yan
    Feng, Xiaoxue
    Pan, Feng
    [J]. REMOTE SENSING, 2022, 14 (02)