TBTA-D2Net: a novel hyperspectral image classification method based on triple-branch ternary-attention mechanism and Dense2Net

ABSTRACT

Recently, there has been a growing interest in the hyperspectral image (HSI) classification methods that employ deep learning techniques in small sample cases. To address issues with network degradation and enhance the extraction of discriminative HSI features, this article proposes a TBTA-D2Net network utilizing a triple-branch ternary-attention mechanism and Dense2Net. Furthermore, a new deep model optimizer named Adan is introduced to improve the training speed of the network model. This article takes spatial information as a two-dimensional vector, extracting spectral features as well as spatial-X and spatial-Y features separately in three branches. Each branch includes a Dense2Net bottleneck module and an attention module. Classification is achieved by fusing the features extracted from the three branches. Experimental results on four public datasets indicate that TBTA-D2Net can achieve competitive results over state-of-the-art methods. The code is available at https://github.com/TeresaTing/TBTA-D2Net.

KEYWORDS:

• Hyperspectral image classification
• deep learning
• three branch network structure
• attention mechanism
• Dense2Net
• small training set

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the HSI datasets utilized in this paper are public datasets.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant 61962048, 61562067 and 52079063; Technological Achievements of Inner Mongolia Autonomous Region of China under Grant 2020CG0054; Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grant 2019JQ06; Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region under Grant NMGIRT2313; Central Public-interest Scientific Institution Basal Research Found under Grant 1610332020020; The Center of Information and Network Technology of Inner Mongolia Agricultural University.