# Active Deep Learning for Classification of Hyperspectral Images

arXiv: https://arxiv.org/abs/1611.10031 · HTML: https://mlsanity.com/paper/1611.10031/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 11 of 11)

- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples](https://mlsanity.com/paper/2112.01800/) (2021)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study](https://mlsanity.com/paper/2110.02580/) (2021)
- [Deep Active Learning for Computer Vision: Past and Future](https://mlsanity.com/paper/2211.14819/) (2022)
- [Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning](https://mlsanity.com/paper/2312.10116/) (2023)
- [Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery](https://mlsanity.com/paper/2404.19043/) (2024)
- [Deep Diffusion Processes for Active Learning of Hyperspectral Images](https://mlsanity.com/paper/2101.03197/) (2021)
- [Image Augmentation for Satellite Images](https://mlsanity.com/paper/2207.14580/) (2022)
- [L-UNet: An LSTM Network for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2603.22842/) (2026)
- [Trusting small training dataset for supervised change detection](https://mlsanity.com/paper/2104.05443/) (2021)

## Abstract (arXiv)

Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.