# A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2105.14327 · HTML: https://mlsanity.com/paper/2105.14327/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [Three dimensional Deep Learning approach for remote sensing image classification](https://mlsanity.com/paper/1806.05824/) (2018)
- [Cascaded Recurrent Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.10858/) (2019)
- [FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification](https://mlsanity.com/paper/2011.05670/) (2020)
- [Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network](https://mlsanity.com/paper/1705.00727/) (2017)
- [Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning](https://mlsanity.com/paper/2012.03439/) (2020)
- [Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.04621/) (2019)
- [Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images](https://mlsanity.com/paper/1904.03982/) (2019)
- [Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.03577/) (2019)
- [Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/1803.09824/) (2018)

## Built on by (most cited first, 13 of 13)

- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [Universal Domain Adaptation for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2301.11387/) (2023)
- [Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.18593/) (2024)
- [SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution](https://mlsanity.com/paper/2310.00413/) (2023)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [Real HSI-MSI-PAN image dataset for the hyperspectral/multi-spectral/panchromatic image fusion and super-resolution fields](https://mlsanity.com/paper/2407.02387/) (2024)
- [C3DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories](https://mlsanity.com/paper/2407.04100/) (2024)
- [Diffusion-based Data Augmentation and Knowledge Distillation with Generated Soft Labels Solving Data Scarcity Problems of SAR Oil Spill Segmentation](https://mlsanity.com/paper/2412.08116/) (2024)
- [Rega-Net:Retina Gabor Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2211.12698/) (2022)
- [Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2302.05297/) (2023)
- [Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime](https://mlsanity.com/paper/2410.07790/) (2024)
- [Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly](https://mlsanity.com/paper/2503.16086/) (2025)
- [Hierarchical Fusion of Local and Global Visual Features with Mixture-of-Experts for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.27155/) (2025)

## Abstract (arXiv)

Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast patch-free global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information. However, FPGA has difficulty extracting the most discriminative features when the sample data is imbalanced. In this paper, a spectral-spatial dependent global learning (SSDGL) framework based on global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods. Code can be obtained at: https://github.com/dengweihuan/SSDGL.