A Deep Double-Channel Dense Network for Hyperspectral Image Classifica-tion

Abstract

Hyperspectral Image (HSI) classification based on deep learning has been an attractive area in recent years. However, as a kind of data-driven algorithm, the deep learning method usually requires numerous computational resources and high-quality labelled datasets, while the expenditures of high-performance computing and data annotation are expensive. In this paper, to reduce the dependence on massive calculation and labelled samples, we propose a deep Double-Channel dense network (DDCD) for Hyperspectral Image Classification. Specifically, we design a 3D Double-Channel dense layer to capture the local and global features of the input. And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs. The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods, which means DDCD owns simpler architecture and higher efficiency. A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance, even though when the absence of labelled samples is severe.

Keywords

• |3d double-channel dense layer|linear attention mechanism|deep learning (dl)|hyperspectral classification