# FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2011.05670 · HTML: https://mlsanity.com/paper/2011.05670/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 28 of 28)

- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery](https://mlsanity.com/paper/2503.21841/) (2025)
- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2304.09915/) (2023)
- [DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2404.00694/) (2024)
- [S2Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18213/) (2024)
- [Dual-stage Hyperspectral Image Classification Model with Spectral Supertoken](https://mlsanity.com/paper/2407.07307/) (2024)
- [Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery](https://mlsanity.com/paper/2308.15081/) (2023)
- [Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification](https://mlsanity.com/paper/2106.13952/) (2021)
- [One-Class Risk Estimation for One-Class Hyperspectral Image Classification](https://mlsanity.com/paper/2210.15457/) (2022)
- [Deep Diversity-Enhanced Feature Representation of Hyperspectral Images](https://mlsanity.com/paper/2301.06132/) (2023)
- [Toward Efficient Hyperspectral Image Processing inside Camera Pixels](https://mlsanity.com/paper/2203.05696/) (2022)
- [HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2107.11979/) (2021)
- [Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.18593/) (2024)
- [Deep Sensing of Urban Waterlogging](https://mlsanity.com/paper/2103.05927/) (2021)
- [Exploiting Frequency Correlation for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2406.00683/) (2024)
- [Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search](https://mlsanity.com/paper/2410.05425/) (2024)
- [A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.01673/) (2024)
- [SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping](https://mlsanity.com/paper/2510.27219/) (2025)
- [Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering](https://mlsanity.com/paper/2604.27364/) (2026)
- [Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2302.05297/) (2023)
- [HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search](https://mlsanity.com/paper/2304.11701/) (2023)
- [Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime](https://mlsanity.com/paper/2410.07790/) (2024)
- [HOpenCls: Training Hyperspectral Image Open-Set Classifiers in Their Living Environments](https://mlsanity.com/paper/2502.15163/) (2025)
- [HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmark](https://mlsanity.com/paper/2607.21050/) (2026)
- [Mapping melliferous tree species in Kenya via one-class classification with hyperspectral unsupervised domain adaptation](https://mlsanity.com/paper/2608.02045/) (2026)
- [Token Clustering and Semantic Sequence Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2609.28580/) (2026)

## Abstract (arXiv)

Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification. In FPGA, an encoder-decoder based FCN is utilized to consider the global spatial information by processing the whole image, which results in fast inference. However, it is difficult to directly utilize the encoder-decoder based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples. To solve the divergence problem and maintain the abilities of FCN of fast inference and global spatial information mining, a global stochastic stratified sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples. This strategy can obtain diverse gradients to guarantee the convergence of the FCN in the FPGA framework. For a better design of FCN architecture, FreeNet, which is a fully end-to-end network for HSI classification, is proposed to maximize the exploitation of the global spatial information and boost the performance via a spectral attention based encoder and a lightweight decoder. A lateral connection module is also designed to connect the encoder and decoder, fusing the spatial details in the encoder and the semantic features in the decoder. The experimental results obtained using three public benchmark datasets suggest that the FPGA framework is superior to the patch-based framework in both speed and accuracy for HSI classification. Code has been made available at: https://github.com/Z-Zheng/FreeNet.