Abstract
Abstract. Using space-borne remote sensing data is widely used for land-cover classification (LCC) due to its ability to provide a big amount of data with a regular temporal revisit time. In recent years, optical and synthetic aperture radar (SAR) imagery have become available for free, and their integration in time series have improved LCC. This research evaluates the classification accuracy using multitemporal (MT) Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Pixel-based LCC is made for S1 and S2 imagery, and for a combination of both datasets with Random Forest (RF) and Extreme Gradient Boosting (XGBoost; XGB). The extent of the study area, is located in the south-east of France, in Lyon. Regardless of LCC using single-date or MT data, the highest classification results were achieved with integrated S1 and S2 imagery and XGB method, whereas overall accuracy (OA) and Kappa coefficient (Kappa) increased from 85.51% to 91.09%, and from 0.81 to 0.88, respectively. Furthermore, the integration of MT imagery significantly improved the classification of urban areas and reduced misclassification between forest and low vegetation. In this paper, in terms of the pixel-based classification, XGB produced slightly better results than RF, and outperformed it in terms of computational time. This research improved LCC with integration of radar and optical MT imagery, which can be useful for areas hampered by a frequent cloud cover. Future work should use the aforementioned data for specific applications in remote sensing, as well as evaluate the classification performance with different approaches, such as neural networks or deep learning.
Highlights
Land-cover classification (LCC) is significant for monitoring urban growth, agricultural planning, and deforestation (Souza, Jr et al, 2013; Veloso et al, 2017; Zakeri et al, 2017)
The results of the LCC using the two machine learning methods described in Section 3 are shown here
Classification accuracy was examined for LCC on multitemporal input data (S1, S2, and their integration) using two classifiers (RF and XGB)
Summary
Land-cover classification (LCC) is significant for monitoring urban growth, agricultural planning, and deforestation (Souza, Jr et al, 2013; Veloso et al, 2017; Zakeri et al, 2017). Satellite imagery acquired from remote sensing (RS) is widely used in LCC and monitoring owing to a big amount of spatial data with a daily revisit time. The usual way of performing classification tasks is the use of optical satellite imagery. Optical RS uses the sun as an external source of irradiance; the acquisition of optical imagery may be limited if the cloud layer is large (Sun et al, 2019). Being an active microwave sensor, synthetic aperture radar (SAR) can provide data acquisition that is independent of solar illumination and cloud cover, as microwave radiation penetrates through clouds. SAR data is sensitive to the surface roughness, textural and dielectric properties of land objects (Feng et al, 2019)
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