Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification

Abstract

Crops mapping unequivocally becomes a daunting task in humid, tropical, or subtropical regions due to unattainability of adequate cloud-free optical imagery. Objective of this study is to evaluate the comparative performance between decision-and pixel-levels data fusion ensemble classified maps using Landsat 8, Landsat 7, and Sentinel-2 data. This research implements parallel and concatenation approach to ensemble classify the images. The multiclassifier system comprises of Maximum Likelihood, Support Vector Machines, and Spectral Information Divergence as base classifiers. Decision-level fusion is achieved by implementing plurality voting method. Pixel-level fusion is achieved by implementing fusion by mosaicking approach, thus appending cloud-free pixels from either Sentinel-2 or Landsat 7. The comparison is based on the assessment of classification accuracy. Overall accuracy results show that decision-level fusion achieved an accuracy of 85.4%, whereas pixel-level fusion classification attained 82.5%, but their respective kappa coefficients of 0.84 and 0.80 but are not significantly different according to Z-test at α = 0.05. F1-score values reveal that decision-level performed better on most individual classes than pixel-level. Regression coefficient between planted areas from both approaches is 0.99. However, Support Vector Machines performed the best of the three classifiers. The conclusion is that both decision-level and pixel-level fusion approaches produced comparable classification results. Therefore, either of the procedures can be adopted in areas with inescapable cloud problems for updating crop inventories and acreage estimation at regional scales. Future work can focus on performing more comparison tests on different areas, run tests using different multiclassifier systems, and use different imagery.

Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage

“…Sentinel is a series of satellites launched by ESA Copernicus [45]. Among them, Sentinel-2 is a series of optical satellite that contains two satellites--Sentinel-2A and Sentinel-2B [46]. In this paper, Sentinel-2A (S2A) products are downloaded through the ESA Copernicus data sharing website ().…”

A CNN-Transformer Hybrid Approach for Crop Classification Using Multitemporal Multisensor Images

“…The accuracy of agricultural land cover mapping is basically positively correlated with the number of multitemporal images, Pax et al [10] found that with the increase in the number of time-series images, the estimation accuracy of land area in agricultural areas in Egypt become higher. In order to obtain sufficient cloud-free remote sensing data in humid, tropical, or subtropical regions, Useya and Chen [11] fuse multitemporal Landsat 8, Landsat 7, and Sentinel-2 data, and obtain more accurate crop maps in Zimbabwe. However, in the critical stages of crop growth, the subtle differences in phenology often have important indications for crop production, such as: the flowering of canola or the tillering stage of transplanted rice.…”

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

“…A number of studies have shown that optical remote sensing data can be used to produce maps with high spatial and spectral resolutions [4] and are effective for gathering various types of biomass information, such as leaf chlorophyll content [5] and leaf area index (LAI) [6]. Indeed, Landsat series data have proven effective for identifying crop types with a high level of accuracy [7,8], and red-edge and shortwave infrared reflectance data are useful for Within this framework, the main objective of the present study was to evaluate the potential of ASNARO-2 data for crop-type classification using machine learning algorithms.…”