Multiple Regularizations Deep Learning for Paddy Growth Stages Classification from LANDSAT-8

TL;DR

This paper presents a deep learning approach using multiple regularizations like Dropout and Batch Normalization to classify paddy growth stages from LANDSAT-8 satellite images, achieving high accuracy.

Contribution

It introduces a novel combination of multiple regularizations in deep learning models for accurate paddy growth stage classification from satellite imagery.

Findings

MLP with multiple regularizations outperforms other models

Dropout and Batch Normalization improve classification accuracy

Deep learning methods are effective for remote sensing crop classification

Abstract

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.