Pointshift: Point-Wise Shift MLP for Pixel-Level Cloud Type Classification in Meteorological Satellite Imagery

Yixiang Huang, Zhaoqing Wang, Xin Jiang, Ming Wu, Chuang Zhang, Jun Guo

Abstract: The deep neural network has recently achieved promising results on cloud type classification, which gets rid of the hand-crafted features and plays an essential role in climate change analysis. Previous methods perform context reasoning with single-scale representation at the centre of the network, which is challenging to capture sufficient contextual information. In this paper, we propose a point-wise shift multi-layer perceptron (MLP) for pixel-level cloud type classification, termed PointShift, which effectively models point-wise and multi-scale neighbour information. We design a shift operation to compose a multi-scale receptive field in a non-parametric manner. Besides, we introduce split attention to improve the interaction of feature channels. Extensive experiments on the Himawari-8 image dataset demonstrate that our proposed architecture achieves the best mIoU of 71.06% and a competitive trade-off between efficiency and performance.