Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

Considering that satellites rarely acquire data from the exact nadir direction, angular effect normalization needs to be conducted as an important preprocessing step to correct reflectance observations from off-nadir directions into the nadir direction. Kernel-based bidirectional reflectance distribution function models have been employed for angular effect correction. The kernels used in the model are often predetermined and fixed for an entire image. However, the fixed kernels are unable to accommodate the various reflective characteristics of different ground cover types present in the imaged area. In this paper, we propose a kernel learning procedure that enables the flexible selection of kernels for different land cover types within a scene. The kernels are selected from kernel dictionaries that contain multiple candidate kernels. The selection is conducted on the superpixel level instead of the pixel level in order to reduce within-class variation and overcome the overfitting problem. Experiments are conducted on multiangular images acquired by the Sentinel-2A satellite over a rural area in southeastern Australia. Cross-validation results show that the proposed method is able to adaptively select appropriate kernels for different land cover types, leading to an improved performance for image normalization.