# SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2104.00341 · HTML: https://mlsanity.com/paper/2104.00341/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Cascaded Recurrent Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.10858/) (2019)
- [Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1805.08620/) (2018)
- [Wavelet Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1707.07394/) (2017)
- [Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.02488/) (2019)
- [A Fast 3D CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/2004.14152/) (2020)

## Built on by (most cited first, 12 of 12)

- [A Hyperspectral and RGB Dataset for Building Facade Segmentation](https://mlsanity.com/paper/2212.02749/) (2022)
- [Wavelength-aware 2D Convolutions for Hyperspectral Imaging](https://mlsanity.com/paper/2209.03136/) (2022)
- [ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2502.16890/) (2025)
- [Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and Pretraining](https://mlsanity.com/paper/2309.11122/) (2023)
- [SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution](https://mlsanity.com/paper/2310.00413/) (2023)
- [Classification of grapevine varieties using UAV hyperspectral imaging](https://mlsanity.com/paper/2401.12851/) (2024)
- [Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation](https://mlsanity.com/paper/2203.12350/) (2022)
- [Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly](https://mlsanity.com/paper/2503.16086/) (2025)
- [SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2510.22607/) (2025)
- [A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification](https://mlsanity.com/paper/2604.23622/) (2026)
- [Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification](https://mlsanity.com/paper/2606.04710/) (2026)
- [Direction-adaptive Mamba: Spatial-Frequency Dual-Domain Collaborative Learning for PolSAR Image Classification](https://mlsanity.com/paper/2607.23464/) (2026)

## Abstract (arXiv)

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatial-spectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods. The code is publicly available in https://github.com/tanmay-ty/SpectralNET.