# 3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2504.10795 · HTML: https://mlsanity.com/paper/2504.10795/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 18 of 18)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity](https://mlsanity.com/paper/2207.03620/) (2022)
- [Wavelet Convolutions for Large Receptive Fields](https://mlsanity.com/paper/2407.05848/) (2024)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [Can Biases in ImageNet Models Explain Generalization?](https://mlsanity.com/paper/2404.01509/) (2024)
- [Wavelet Feature Maps Compression for Image-to-Image CNNs](https://mlsanity.com/paper/2205.12268/) (2022)
- [Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks](https://mlsanity.com/paper/2202.06458/) (2022)
- [DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2307.06667/) (2023)
- [Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group Convolution](https://mlsanity.com/paper/2307.07720/) (2023)
- [Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification](https://mlsanity.com/paper/2502.17879/) (2025)
- [Efficient Dynamic Attention 3D Convolution for Hyperspectral Image Classification](https://mlsanity.com/paper/2503.23472/) (2025)
- [Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.04463/) (2025)

## Built on by (most cited first, 5 of 5)

- [Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.13045/) (2025)
- [Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification](https://mlsanity.com/paper/2504.15155/) (2025)
- [Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive Gating](https://mlsanity.com/paper/2506.08324/) (2025)
- [MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture](https://mlsanity.com/paper/2507.04409/) (2025)
- [FSCE: A Target-Aware Frequency-Spatial Collaborative Enhancement Framework for Noise-Resilient SAR ATR](https://mlsanity.com/paper/2603.21565/) (2026)

## Abstract (arXiv)

Deep neural networks face numerous challenges in hyperspectral image classification, including high-dimensional data, sparse ground object distributions, and spectral redundancy, which often lead to classification overfitting and limited generalization capability. To better adapt to ground object distributions while expanding receptive fields without introducing excessive parameters and skipping redundant information, this paper proposes WCNet, an improved 3D-DenseNet model integrated with wavelet transforms. We introduce wavelet transforms to effectively extend convolutional receptive fields and guide CNNs to better respond to low frequencies through cascading, termed wavelet convolution. Each convolution focuses on different frequency bands of the input signal with gradually increasing effective ranges. This process enables greater emphasis on low-frequency components while adding only a small number of trainable parameters. This dynamic approach allows the model to flexibly focus on critical spatial structures when processing different regions, rather than relying on fixed receptive fields of single static kernels. The Wavelet Conv module enhances model representation capability by expanding receptive fields through 3D wavelet transforms without increasing network depth or width. Experimental results demonstrate superior performance on the IN, UP, and KSC datasets, outperforming mainstream hyperspectral image classification methods.