Kernel-based Regularized Learning for Time-Invariant Detection of Paddy Growth Stages from MODIS Data
| Title: Kernel-based Regularized Learning for Time-Invariant Detection of Paddy Growth Stages from MODIS Data |
| Author: Sidik Mulyono, Harisno, Mahfudz Amri, M. Ivan Fanany, T. Basaruddin |
| Abstract: Most current studies have been applying high temporal resolution satellite data for determining paddy crop phenology, that derive into a crtain vegetation indices, by using some filtering and smoothing techniques combine 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 kernel-based 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 readial basis function (RBF-SVM). All dta samples are divided into training (25\%) and testing (75%) sampling, and all models are trained and tested through 10-rounds random bootstraping. The best model for each classifier method is defined as the one which has the higest kappa coefficient during testing. The experimental results show that the classification accuracy of each classifiers on testing are high competitive, i.e., 84%, 84%, and 85%, respectively. |
| Keywords: Remote sensing, MODIS, Phenology, Paddi growth stages, Kernel based learner |
| Corresponding Author: Harisno (harisno@binus.edu) |
| Conference: Intelligent Information and Database Systems |
