Extracting vegetation information from high dynamic range images with shadows: A comparison between deep learning and threshold methods

Due to factors such as light angle and vegetation density, shadows can exist in ground vegetation images; this can lead to highly varying natural illumination in the images, which greatly limits the accuracy of fractional vege-tation cover (FVC) estimation. While high dynamic range (HDR) images can reduce the extreme illumination contrast, the characteristic vegetation information in shadow areas is often lost. At present, most threshold methods do not fully utilize the spatial features of vegetation, and the results of vegetation classification under complex shadow conditions are associated with large errors. Thus, two deep learning methods (the HDR U-Net method and HDR U-Net++ method) are proposed in this study based on fully convolutional neural networks to extract vegetation information from HDR images under shadow conditions. These methods are compared with threshold methods. For full HDR images, the kappa coefficient and mean intersection over union (MIoU) of the HDR U-Net method and HDR U-Net++ method are 0.926, 0.931, and 0.874, 0.887, respectively. The FVC estimation accuracy and vegetation segmentation effect of these two deep learning methods are better than those of the three threshold methods considered. In addition, compared with normal exposure (NE) images, camera -based HDR images improve the extraction accuracy of deep learning methods and threshold methods for vegetation under shadow conditions. The results of this study suggest that deep learning methods are not affected by the threshold determination strategy selected and can more completely extract vegetation feature information from HDR images. The classification effect of vegetation under shadow conditions is effectively improved, and the vegetation segmentation and FVC estimation results are more precise. The combination of HDR images and deep learning methods can be applied to field scenes with complex lighting, which is beneficial to improve the ground data verification of remote sensing FVC products.