# Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images

arXiv: https://arxiv.org/abs/2301.06874 · HTML: https://mlsanity.com/paper/2301.06874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 17 Jan 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)](https://mlsanity.com/paper/2003.02822/) (2020)
- [HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image](https://mlsanity.com/paper/1802.10478/) (2018)
- [Training Generative Adversarial Networks in One Stage](https://mlsanity.com/paper/2103.00430/) (2021)

## Built on by (most cited first, 1 of 1)

- [Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime](https://mlsanity.com/paper/2410.07790/) (2024)

## Abstract (arXiv)

With their combined spectral depth and geometric resolution, hyperspectral remote sensing images embed a wealth of complex, non-linear information that challenges traditional computer vision techniques. Yet, deep learning methods known for their representation learning capabilities prove more suitable for handling such complexities. Unlike applications that focus on single-label, pixel-level classification methods for hyperspectral remote sensing images, we propose a multi-label, patch-level classification method based on a two-component deep-learning network. We use patches of reduced spatial dimension and a complete spectral depth extracted from the remote sensing images. Additionally, we investigate three training schemes for our network: Iterative, Joint, and Cascade. Experiments suggest that the Joint scheme is the best-performing scheme; however, its application requires an expensive search for the best weight combination of the loss constituents. The Iterative scheme enables the sharing of features between the two parts of the network at the early stages of training. It performs better on complex data with multi-labels. Further experiments showed that methods designed with different architectures performed well when trained on patches extracted and labeled according to our sampling method.