# Is Pretraining Necessary for Hyperspectral Image Classification?

arXiv: https://arxiv.org/abs/1901.08658 · HTML: https://mlsanity.com/paper/1901.08658/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.03144/) (2022)
- [Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation](https://mlsanity.com/paper/2202.03968/) (2022)
- [Hyperspectral Benchmark: Bridging the Gap between HSI Applications through Comprehensive Dataset and Pretraining](https://mlsanity.com/paper/2309.11122/) (2023)
- [A Comparative Analysis of CNN-Based Pretrained Models for the Detection and Prediction of Monkeypox](https://mlsanity.com/paper/2302.10277/) (2023)

## Abstract (arXiv)

We address two questions for training a convolutional neural network (CNN) for hyperspectral image classification: i) is it possible to build a pre-trained network? and ii) is the pre-training effective in furthering the performance? To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset. This approach effectively resolves the architectural issue that arises when transferring meaningful information between the source and the target networks. To answer the second question, we carried out several ablation experiments. Based on the experimental results, a network trained from scratch performs as good as a network fine-tuned from a pre-trained network. However, we observed that pre-training the network has its own advantage in achieving better performances when deeper networks are required.