WIANet: A Wavelet-Inspired Attention-Based Convolution Neural Network for Land Cover Classification

Abstract

General convolutional neural networks (CNNs) process the data in the spatial domain and lack efficacy in modeling the spectral context in multispectral image processing task. To address this limitation, in this letter, we propose a wavelet-inspired attention-based convolution neural network (WIANet) architecture that combines the modeling of the spatio-spectral resolution of multispectral remote sensing (RS) images with a wavelet convolution and attention unit into a single deep learning architecture for land cover classification. Our aim is to add the characteristics of both wavelet transform (WT) and attention mechanism into an UNet-based architecture to better exploit the spectral and texture information for distinguishing classes with high similarity in the spectral signatures. We evaluate the performance of the proposed approach on a multilabel multispectral Sentinel-2 dataset. The experiment shows that the proposed approach performs better than the reference methods in different conditions on the number of training samples.