Abstract
Mapping of green vegetation in urban areas using remote sensing techniques can be used as a tool for integrated spatial planning to deal with urban challenges. In this context, multitemporal (MT) synthetic aperture radar (SAR) data have not been equally investigated, as compared to optical satellite data. This research compared various machine learning methods using single-date and MT Sentinel-1 (S1) imagery. The research was focused on vegetation mapping in urban areas across Europe. Urban vegetation was classified using six classifiers—random forests (RF), support vector machine (SVM), extreme gradient boosting (XGB), multi-layer perceptron (MLP), AdaBoost.M1 (AB), and extreme learning machine (ELM). Whereas, SVM showed the best performance in the single-date image analysis, the MLP classifier yielded the highest overall accuracy in the MT classification scenario. Mean overall accuracy (OA) values for all machine learning methods increased from 57% to 77% with speckle filtering. Using MT SAR data, i.e., three and five S1 imagery, an additional increase in the OA of 8.59% and 13.66% occurred, respectively. Additionally, using three and five S1 imagery for classification, the F1 measure for forest and low vegetation land-cover class exceeded 90%. This research allowed us to confirm the possibility of MT C-band SAR imagery for urban vegetation mapping.
Highlights
Remote sensing could provide reliable land-cover classification maps, through the active microwave and passive optical sensors, which could be used for a wide range of applications
In order to assess the performance of the evaluated methods in different steps of the research, mean values of accuracy metrics for all three study areas were calculated
We presented a comparative assessment of six machine learning methods using multitemporal (MT) synthetic aperture radar (SAR) imagery for urban vegetation mapping
Summary
Remote sensing could provide reliable land-cover classification maps, through the active microwave and passive optical sensors, which could be used for a wide range of applications. Urban areas are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales [2]. In this context, a multispectral optical image is easy for interpretation and classification, but often climate conditions limit the utilization of this satellite imagery [3]. The speckle noise degrades the quality of acquired imagery, causing difficulties for both manual and automatic image interpretation [7]. Speckle filtering is required for classification tasks, especially for detecting vegetation in urban systems whose components differ at various scales (urban forest, green roofs, urban gardens, parks). Spatial filters reduce the noise by using smoothing windows based on a weighted summation of neighboring pixels
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