# Hyperspectral Image Classification with Attention Aided CNNs

arXiv: https://arxiv.org/abs/2005.11977 · HTML: https://mlsanity.com/paper/2005.11977/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 18 of 18)

- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2304.09915/) (2023)
- [Dual-stage Hyperspectral Image Classification Model with Spectral Supertoken](https://mlsanity.com/paper/2407.07307/) (2024)
- [Attention mechanisms and deep learning for machine vision: A survey of the state of the art](https://mlsanity.com/paper/2106.07550/) (2021)
- [Dual Selective Fusion Transformer Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.03171/) (2024)
- [Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification](https://mlsanity.com/paper/2106.13952/) (2021)
- [DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification](https://mlsanity.com/paper/2312.12441/) (2023)
- [Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.18593/) (2024)
- [GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification](https://mlsanity.com/paper/2204.10099/) (2022)
- [Probabilistic Deep Metric Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2211.08349/) (2022)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [PIF-Net: Ill-Posed Prior Guided Multispectral and Hyperspectral Image Fusion via Invertible Mamba and Fusion-Aware LoRA](https://mlsanity.com/paper/2508.00453/) (2025)
- [Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network](https://mlsanity.com/paper/2201.02821/) (2022)
- [A comparative study of attention mechanism and generative adversarial network in facade damage segmentation](https://mlsanity.com/paper/2209.13283/) (2022)
- [ChromaFormer: A Scalable and Accurate Transformer Architecture for Land Cover Classification](https://mlsanity.com/paper/2503.08534/) (2025)
- [CoFusion: Multispectral and Hyperspectral Image Fusion via Spectral Coordinate Attention](https://mlsanity.com/paper/2604.10584/) (2026)
- [SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification](https://mlsanity.com/paper/2604.15828/) (2026)
- [MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification](https://mlsanity.com/paper/2604.26138/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are firstly cropped from the hyperspectral image and then fed into CNNs to extract spectral and spatial features. It is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. If fully explored, this prior information will help improve the learning capacity of CNNs. Along this direction, we propose an attention aided CNN model for spectral-spatial classification of hyperspectral images. Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively. Both of them are based on the traditional CNN model, and incorporate attention modules to aid networks focus on more discriminative channels or positions. In the final classification phase, the spectral classification result and the spatial classification result are combined together via an adaptively weighted summation method. To evaluate the effectiveness of the proposed model, we conduct experiments on three standard hyperspectral datasets. The experimental results show that the proposed model can achieve superior performance compared to several state-of-the-art CNN-related models.