Different colours of shadows: classification of UAV images

TLDR: In this study, the Maximum Likelihood (ML) and Support Vector Machine (SVM) classifiers were used to classify a UAV image acquired using a red–green–blue (RGB) camera over the Old Woman Creek National Estuarine Research Reserve in Ohio, USA.

Abstract: Due to their low light conditions, shadows reduce the accuracy of feature extraction and change detection in remote-sensing images. Unmanned aerial vehicles UAVs are capable of acquiring images that have a resolution of several centimetres and removing shadows is a challenge. In this study, the Maximum Likelihood ML and Support Vector Machine SVM classifiers were used to classify a UAV image acquired using a red–green–blue RGB camera over the Old Woman Creek National Estuarine Research Reserve in Ohio, USA. The impact of shadows on the classification process was explored for different pixel sizes ranging from 0.03 to 1.00 m. The SVM generated higher overall accuracy OA at finer spatial resolution 0.25–0.50 m, while the optimal spatial resolution for the ML classifier was 1.00 m. The percentage of shadow coverage increased with spatial resolution for both classifiers 1.71% for ML and 6.63% for SVM. Shadows were detected and extracted using two approaches: a as a separate class using regions of interests ROIs observed in the image, and b by applying a segmentation threshold of 0.3 to visible atmospherically resistant index VARI. The extracted shadows were separately classified using ROIs selected from shaded surfaces, and then removed using the fusion of RGB reflectance, VARI, and digital surface model DSM images. The OA of classified shadows reached 91.50%. OAs of merged sunlit and shadow classified images improved for 18.48% for SVM, and 17.62% for the ML classifier. VARI accurately captures shadows, and when fused with RGB reflectance and DSM, it intensifies their low signal and enhances classification. Whether used to capture or to remove shadows, VARI serves as an effective ‘shadow index’. Shadows create obstacles to remote-sensing processing; however, their spectral information should not be neglected as both shadows and sunlit areas are important for ecological processes such as photosynthesis, carbon balance, evapotranspiration, fish abundance, and more.