# Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2201.08938 · HTML: https://mlsanity.com/paper/2201.08938/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [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)
- [SGAN: An Alternative Training of Generative Adversarial Networks](https://mlsanity.com/paper/1712.02330/) (2017)

## Built on by (most cited first, 8 of 8)

- [Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2310.12452/) (2023)
- [Hyperspectral Image Classification With Contrastive Graph Convolutional Network](https://mlsanity.com/paper/2205.11237/) (2022)
- [Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging](https://mlsanity.com/paper/2505.00805/) (2025)
- [A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset](https://mlsanity.com/paper/2407.16384/) (2024)
- [Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.19699/) (2025)
- [SpectralTrain: A Universal Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2511.16084/) (2025)
- [Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification](https://mlsanity.com/paper/2505.04003/) (2025)
- [Photovoltaic Defect Image Generator with Boundary Alignment Smoothing Constraint for Domain Shift Mitigation](https://mlsanity.com/paper/2505.06117/) (2025)

## Abstract (arXiv)

In recent years, hyperspectral image (HSI) classification based on generative adversarial networks (GAN) has achieved great progress. GAN-based classification methods can mitigate the limited training sample dilemma to some extent. However, several studies have pointed out that existing GAN-based HSI classification methods are heavily affected by the imbalanced training data problem. The discriminator in GAN always contradicts itself and tries to associate fake labels to the minority-class samples, and thus impair the classification performance. Another critical issue is the mode collapse in GAN-based methods. The generator is only capable of producing samples within a narrow scope of the data space, which severely hinders the advancement of GAN-based HSI classification methods. In this paper, we proposed an Adaptive DropBlock-enhanced Generative Adversarial Networks (ADGAN) for HSI classification. First, to solve the imbalanced training data problem, we adjust the discriminator to be a single classifier, and it will not contradict itself. Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue. The AdapDrop generated drop masks with adaptive shapes instead of a fixed size region, and it alleviates the limitations of DropBlock in dealing with ground objects with various shapes. Experimental results on three HSI datasets demonstrated that the proposed ADGAN achieved superior performance over state-of-the-art GAN-based methods. Our codes are available at https://github.com/summitgao/HC_ADGAN