Kernel-based regularized learning for time-invariant detection of paddy growth stages from MODIS data

Sidik Mulyono, Harisno, Mahfudz Amri, Mohamad Ivan Fanany, T. Basaruddin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most current studies have been applying high temporal resolution satellite data for determining paddy crop phenology, that derive into a certain vegetation indices, by using some filtering and smoothing techniques combined with threshold methods. In this paper, we introduce a time invariant detection of paddy growth stages using single temporal resolution satellite data instead of high temporal resolution with complex cropping pattern. Our system is a kernelbased regularized learner that predicts paddy growth stages from six-bands spectral of Moderate Resolution Image Spectroradiometer (MODIS) satellite data. It evaluates three Kernel-based Regularized (KR) classification methods, i.e. Principal Component Regression (KR-PCR), Extreme Learning Machine (KR-ELM), and Support Vector Machine with radial basis function (RBFSVM). All data samples are divided into training (25%) and testing (75%) sampling, and all models are trained and tested through 10-rounds random bootstrap re-sampling method to obtain more variety on hypothesis models during learning. The best model for each classifier method is defined as the one which has the highest kappa coefficient during testing. The experimental results show that the classification accuracy of each classifiers on testing are high competitive, i.e. 84.08%, 84.04%, and 84.95% respectively.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 7th Asian Conference, ACIIDS 2015, Proceedings
EditorsNgoc Thanh Nguyen, Raymond Kosala, Ngoc Thanh Nguyen, Bogdan Trawiński
PublisherSpringer Verlag
Pages513-525
Number of pages13
ISBN (Electronic)9783319157016
DOIs
Publication statusPublished - 1 Jan 2015
Event7th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2015 - Bali, Indonesia
Duration: 23 Mar 201525 Mar 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9011
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2015
CountryIndonesia
CityBali
Period23/03/1525/03/15

Keywords

  • Kernel based learner
  • MODIS
  • Paddy growth stages
  • Phenology
  • Remote sensing

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  • Cite this

    Mulyono, S., Harisno, Amri, M., Fanany, M. I., & Basaruddin, T. (2015). Kernel-based regularized learning for time-invariant detection of paddy growth stages from MODIS data. In N. T. Nguyen, R. Kosala, N. T. Nguyen, & B. Trawiński (Eds.), Intelligent Information and Database Systems - 7th Asian Conference, ACIIDS 2015, Proceedings (pp. 513-525). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9011). Springer Verlag. https://doi.org/10.1007/978-3-319-15702-3_50