  This study uses remote sensing technology that can provide information about
the condition of the earth's surface area, fast, and spatially. The study area
was in Karawang District, lying in the Northern part of West Java-Indonesia. We
address a paddy growth stages classification using LANDSAT 8 image data
obtained from multi-sensor remote sensing image taken in October 2015 to August
2016. This study pursues a fast and accurate classification of paddy growth
stages by employing multiple regularizations learning on some deep learning
methods such as DNN (Deep Neural Networks) and 1-D CNN (1-D Convolutional
Neural Networks). The used regularizations are Fast Dropout, Dropout, and Batch
Normalization. To evaluate the effectiveness, we also compared our method with
other machine learning methods such as (Logistic Regression, SVM, Random
Forest, and XGBoost). The data used are seven bands of LANDSAT-8 spectral data
samples that correspond to paddy growth stages data obtained from i-Sky (eye in
the sky) Innovation system. The growth stages are determined based on paddy
crop phenology profile from time series of LANDSAT-8 images. The classification
results show that MLP using multiple regularization Dropout and Batch
Normalization achieves the highest accuracy for this dataset.
