Classification of Rice-Plant Growth Phase using Supervised Random Forest Method Based on Landsat-8 Multitemporal Data

Dwi Wahyu Triscowati and Bagus Sartono and Anang Kurnia and Dede Dirgahayu and Arie Wahyu Wijayanto (2019) Classification of Rice-Plant Growth Phase using Supervised Random Forest Method Based on Landsat-8 Multitemporal Data. International Journal of Remote Sensing and Earth Sciences, 16 (2). pp. 187-196. ISSN 0216-6739

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Abstract

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.

Item Type: Article
Uncontrolled Keywords: rice-plant classification, temporal autocorrelation, temporal features engineering, random forest, Landsat-8
Subjects: Teknologi Penginderaan Jauh > Penelitian, Pengkajian, dan Pengembangan > Teknologi dan Data Penginderaan Jauh > Perolehan Data > Satelit
Divisions: Deputi Penginderaan Jauh > Pusat Pemanfaatan Penginderaan jauh
Depositing User: Dinar Indrasasi
Date Deposited: 04 Aug 2021 02:14
Last Modified: 23 Sep 2021 09:36
URI: http://repositori.lapan.go.id/id/eprint/1088

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