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Earth Science Informatics

, Volume 12, Issue 4, pp 429–446 | Cite as

Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation

  • Supattra Puttinaovarat
  • Paramate HorkaewEmail author
Research Article

Abstract

Characterization of oil palm plantation is a crucial step toward many geographical based management strategies, ranging from determining regional planting and appropriate species to irrigation and logistics planning. Accurate and most updated plantation identification enables well informed and effective measures for such schemes. This paper proposes a computerized method for detecting oil-palm plantation from remotely sensed imagery. Unlike other existing approaches, where imaging features were retrieved from spectral data and then trained with a machine learning box for region of interest extraction, this paper employed 2-stage detection. Firstly, a deep learning network was employed to determine a presence of oil-palm plantation in a generic Google satellite image. With irrelevant samples being disregarded and thus the problem space being so contained, the images with detected oil-palm had their plantation delineated at higher accuracy by using a support vector machine, based on Gabor texture descriptor. The proposed coupled detection-delineation was benchmarked against different feature descriptors and state-of-the-art supervised and unsupervised machine learning techniques. The validation was made by comparing the extraction results with those ground surveyed by an authority. It was shown in the experiments that it could detect and delineate the plantations with an accuracy of 92.29% and precision, recall and Kappa of 91.16%, 84.97%, and 0.81, respectively.

Keywords

Oil palm plantation Texture analysis Gabor wavelet Deep learning GoogLeNet 

Notes

Acknowledgements

The authors would like to thank the Google Inc. and the Land Development Department of Thailand, respectively, for providing the remotely sensed data (Google Satellite Image) and surveyed information on oil palm plantation employed in preparation of the paper.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Science and Industrial TechnologyPrince of Songkla UniversitySurat ThaniThailand
  2. 2.School of Computer Engineering, Institute of EngineeringSuranaree University of Technology Nakhon RatchasimaNakhon RatchasimaThailand

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