# HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image

arXiv: https://arxiv.org/abs/1802.10478 · HTML: https://mlsanity.com/paper/1802.10478/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 15 of 15)

- [FOCUS: Fused Observation of Channels for Unveiling Spectra](https://mlsanity.com/paper/2507.14787/) (2025)
- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)
- [HyperKAN: Kolmogorov-Arnold Networks make Hyperspectral Image Classificators Smarter](https://mlsanity.com/paper/2407.05278/) (2024)
- [Toward Efficient Hyperspectral Image Processing inside Camera Pixels](https://mlsanity.com/paper/2203.05696/) (2022)
- [3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.13252/) (2024)
- [HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2107.11979/) (2021)
- [Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2205.09250/) (2022)
- [Supervised Embedded Methods for Hyperspectral Band Selection](https://mlsanity.com/paper/2401.11420/) (2024)
- [Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images](https://mlsanity.com/paper/2301.06874/) (2023)
- [Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study](https://mlsanity.com/paper/2206.09414/) (2022)
- [Agricultural Plantation Classification using Transfer Learning Approach based on CNN](https://mlsanity.com/paper/2206.09420/) (2022)
- [AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging](https://mlsanity.com/paper/2409.09386/) (2024)
- [Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime](https://mlsanity.com/paper/2410.07790/) (2024)
- [Explainability-Driven Dimensionality Reduction for Hyperspectral Imaging](https://mlsanity.com/paper/2509.02340/) (2025)
- [Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data](https://mlsanity.com/paper/2602.10745/) (2026)

## Abstract (arXiv)

With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN. Firstly, the spectral-spatial feature is extracted from a target pixel and its neighbors. Then, a number of one-dimensional feature maps, obtained by convolution operation on spectral-spatial features, are stacked into a two-dimensional matrix. Finally, the two-dimensional matrix considered as an image is fed into standard CNN. This is why we call it HSI-CNN. In addition, we also implements two depth network classification models, called HSI-CNN+XGBoost and HSI-CapsNet, in order to compare the performance of our framework. Experiments show that the performance of hyperspectral image classification is improved efficiently with HSI-CNN framework. We evaluate the model's performance using four popular HSI datasets, which are the Kennedy Space Center (KSC), Indian Pines (IP), Pavia University scene (PU) and Salinas scene (SA). As far as we concerned, HSI-CNN has got the state-of-art accuracy among all methods we have known on these datasets of 99.28%, 99.09%, 99.42%, 98.95% separately.