Precipitation is a crucial link in the hydrological cycle, and its spatial and temporal variations are enormous. A knowledge of the amount of regional rainfall is essential to the welfare of society. Rainfall can be estimated remotely, either from ground-based weather radars or from satellite. Despite the large amount of available data provided by satellites, most of them are unlabeled, and the acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. The semi-supervised learners combine both labeled and unlabeled data to perform the classification task. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S3VM). The S3VM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unlabeled data. The accuracies obtained for SVM and S3VM were, respectively, 90.6% and 95.96%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rainfall Estimation Using Transductive Learning




    Publication date :

    2008-05-01


    Size :

    246305 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Contrastive Learning and Cycle Consistency-Based Transductive Transfer Learning for Target Annotation

    Sami, Shoaib Meraj / Hasan, Md Mahedi / Nasrabadi, Nasser M. et al. | IEEE | 2024



    Fault diagnosis using Neuro-Fuzzy Transductive Inference algorithm

    Bo Zhang, / Jianjun Luo, / Zhiqiu Chen, et al. | IEEE | 2008


    Large-scale image retrieval using transductive support vector machines

    Cevikalp, Hakan / Elmas, Merve / Ozkan, Savas | British Library Online Contents | 2018


    Texture Image Segmentation: An Interactive Framework Based on Adaptive Features and Transductive Learning

    Xiang, Shiming / Nie, Feiping / Zhang, Changshui | Springer Verlag | 2006