Classification of small agricultural fields using combined Landsat-8 and RapidEye imagery: case study of northern Serbia

Vladimir Crnojevic,Borislav Brunet,Predrag Lugonja,Branko Brkljac

doi:10.1117/1.jrs.8.083512

Abstract

A pixel-based cropland classification study based on the fusion of data from satellite images with different resolutions is presented. It is based on a time series of multispectral images acquired at different resolutions by different imaging instruments, Landsat-8 and RapidEye. The proposed data fusion method capabilities are explored with the aim of overcoming the shortcomings of different instruments in the particular cropland classification scenario characterized by the very small size of crop fields over the chosen agricultural region situated in the plains of Vojvodina in northern Serbia. This paper proposes a data fusion method that is successfully utilized in combination with arobust random forest classifier in improving the overall classification performance, as well as in enabling application of satellite imagery with a coarser spatial resolution in the given specific cropland classification task. The developed method effectively exploits available data and provides an improvement over the existing pixel-based classification approaches through the combination of different data sources. Another contribution of this paper is the employment of crowdsourcing in the process of reference data collection via dedicated smartphone application.

Highlights

The automatic cropland classification based on the data from spaceborne imagery is one of the most important sources of valuable information about the composition and the development of a variety of crops grown in different agricultural regions around the world
The fact that most of these satellite imagery systems are commercial represents one of the obstacles for their widespread use and produces the need for the effective and efficient use of a vast amount of freely available data, like the high-quality data already available through the Landsat program[13] or the data that are planned to be published under similar terms by the upcoming Sentinel missions.[14]. In line with these efforts, this paper presents a pixel-based cropland classification study that utilizes a time series of multispectral images with different properties which were acquired at different resolutions by different imaging instruments—Landsat-815 and RapidEye.[16]
The proposed data fusion method provides an alternative to the standard approach that would require use of commercial satellite imagery in the form of a high-resolution multispectral time series, which are, not very affordable, especially for public services with constrained budgets

Summary

Introduction

The automatic cropland classification based on the data from spaceborne imagery is one of the most important sources of valuable information about the composition and the development of a variety of crops grown in different agricultural regions around the world. The recent advances in the satellite imaging technology provide researchers and practitioners with ever more data in both quantitative and qualitative ways. This opens new opportunities for the extraction of meaningful and useful information, but it creates new challenges regarding the choice and the development of appropriate methods for their processing. While satellite imagery for crop identification and crop-covered area estimation is a practice with more

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Conclusion