Location Soft-Aggregation-Based Band Weighting for Hyperspectral Image Classification

Abstract

Hyperspectral images (HSIs) comprise hundreds of continuous spectral bands. How to effectively exploit the abundant spectral features of HSI to improve its classification accuracy is the focus of the research. Band weighting (BW) is extensively used due to its ability to emphasize usefully and suppress noisy bands adaptively. Most proposed works aggregate global information to construct band representation vectors in simple ways such as global averaging pooling. Those ways are not capable of retaining a more discriminating feature. Furthermore, modeling for interpixel positional relationships is something they have not considered. To address these problems, we propose a position embedding and importance aggregation BW module. The position embedding section encodes the position information by two 1-D features so that remote dependencies in one spatial direction can be obtained while retaining accurate position information in the other spatial direction. The importance aggregation section aggregates the global information. Finally, a group of weights is learned to recalibrate the raw input. Experiments on three public datasets of HSI demonstrate that our methods obtain competitive results compared to other methods.