# Three dimensional Deep Learning approach for remote sensing image classification

arXiv: https://arxiv.org/abs/1806.05824 · HTML: https://mlsanity.com/paper/1806.05824/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2018.

## Built on by (most cited first, 30 of 51)

- [Multimodal Fusion Transformer for Remote Sensing Image Classification](https://mlsanity.com/paper/2203.16952/) (2022)
- [Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models](https://mlsanity.com/paper/2301.08072/) (2023)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [Benchmarking and scaling of deep learning models for land cover image classification](https://mlsanity.com/paper/2111.09451/) (2021)
- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.05100/) (2024)
- [GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.08255/) (2024)
- [Dual Selective Fusion Transformer Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.03171/) (2024)
- [Wavelength-aware 2D Convolutions for Hyperspectral Imaging](https://mlsanity.com/paper/2209.03136/) (2022)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)
- [Deep Diversity-Enhanced Feature Representation of Hyperspectral Images](https://mlsanity.com/paper/2301.06132/) (2023)
- [Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification](https://mlsanity.com/paper/2409.01236/) (2024)
- [Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification](https://mlsanity.com/paper/2203.04771/) (2022)
- [Toward Efficient Hyperspectral Image Processing inside Camera Pixels](https://mlsanity.com/paper/2203.05696/) (2022)
- [Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders](https://mlsanity.com/paper/2207.07803/) (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)
- [Language-Depth Navigated Thermal and Visible Image Fusion](https://mlsanity.com/paper/2503.08676/) (2025)
- [Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios](https://mlsanity.com/paper/2506.18682/) (2025)
- [Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN](https://mlsanity.com/paper/2101.10532/) (2021)
- [Triplet-Watershed for Hyperspectral Image Classification](https://mlsanity.com/paper/2103.09384/) (2021)
- [Probabilistic Deep Metric Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2211.08349/) (2022)
- [HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding](https://mlsanity.com/paper/2311.14899/) (2023)
- [Supervised Embedded Methods for Hyperspectral Band Selection](https://mlsanity.com/paper/2401.11420/) (2024)
- [Investigation of Hierarchical Spectral Vision Transformer Architecture for Classification of Hyperspectral Imagery](https://mlsanity.com/paper/2409.09244/) (2024)
- [Learning Hyperspectral Images with Curated Text Prompts for Efficient Multimodal Alignment](https://mlsanity.com/paper/2509.22697/) (2025)
- [A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2205.12459/) (2022)
- [Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2310.18549/) (2023)

## Abstract (arXiv)

Recently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited faced to the rich spatio-spectral content of today's large datasets. It would seem intriguing to resort to Deep Learning (DL) based approaches at this stage with regards to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS datasets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is firstly to explore the performance of DL architectures for the RS hyperspectral dataset classification and secondly to introduce a new three-dimensional DL approach that enables a joint spectral and spatial information process. A set of three-dimensional schemes is proposed and evaluated. Experimental results based on well knownhyperspectral datasets demonstrate that the proposed method is able to achieve a better classification rate than state of the art methods with lower computational costs.