Attention Networks for Band Weighting And Selection In Hyperspectral Remote Sensing Image Classification

Abstract

Hyperspectral imaging is widely used in remote sensing because of its capability to capture the detailed spectral reflection of the ground object. The acquired rich band information brings significant benefits to better discriminate the target pixels. However, this imaging method also introduces redundant and noisy bands which may lower the classification accuracy. In addition, the contribution of different bands towards the final classification task are not necessarily the same. Therefore, band weighting and band selection are often adopted to model the relationship among the bands and remove the irrelevant ones. Attention mechanism is a method in neural networks to guide the algorithm to focus on the important information. In this paper, we propose an attention based deep learning framework to achieve band weighting and selection. The experimental results on two hyperspectral image datasets show the effectiveness of the proposed framework.