Enhanced Multi-Dimensional and Multi-Grained Cascade Forest for Cloud/Snow Recognition Using Multispectral Satellite Remote Sensing Imagery

Abstract

Cloud/snow recognition is one application of satellite remote sensing imagery in natural disaster monitoring. Deep learning technology has contributed to the improvement of the performance of cloud/snow recognition. However, deep learning-based methods cannot well balance the performance and efficiency of cloud/snow recognition. In this paper, an augmented multi-dimensional and multi-grained Cascade Forest is proposed for cloud/snow recognition. The multi-dimensional deep forest structure with the representation learning ability allows it to capture the spatial and spectral information of cloud/snow satellite imagery accordingly equipped with good recognition efficiency. Besides, a simple augmentation Random Erasing method is introduced for enhancing the robustness of cloud/snow recognition. The experimental results on the HJ-1A/1B dataset show that the proposed method improves the performance of cloud/snow recognition by extracting spectral information from multi-spectral satellite imagery. In addition, based on the tree-based structure, the proposed method well balances the performance and efficiency of cloud/snow recognition, which can be considered as an alternative to the Neural Network for cloud/snow recognition.