# Is Pretraining Necessary for Hyperspectral Image Classification?

## TL;DR

This paper investigates whether pretraining is necessary for hyperspectral image classification, finding that training from scratch can match pretraining performance, but pretraining benefits deeper networks.

## Contribution

It introduces a method for pretraining on multiple datasets and evaluates the effectiveness of pretraining versus training from scratch for hyperspectral CNNs.

## Key findings

- Training from scratch performs as well as pretraining.
- Pretraining offers advantages for deeper networks.
- Pretraining helps when deeper architectures are used.

## Abstract

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.