Rainfall Estimation Using Transductive Learning

• Published in: 2008 Congress on Image and Signal Processing Vol. 4; pp. 631 - 634
• Main Authors: Martins de Freitas, Greice, Heuminski de Ávila, Ana Maria, Papa, João Paulo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2008
• Subjects: Agriculture; Humans; Infrared imaging; Meteorological radar; Meteorology; NOAA; rainfall estimation; Satellites; semi-supervised support vector machines; Semisupervised learning; Support vector machine classification; Support vector machines; transductive learning; Virtual manufacturing
• ISBN: 9780769531199; 0769531199
• DOI: 10.1109/CISP.2008.561

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%.