# Ladder Networks for Semi-Supervised Hyperspectral Image Classification

Source: [Ladder Networks for Semi-Supervised Hyperspectral Image Classification](<https://www.sota2.com/research/paper/68fa651c1e209131cea08640>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:18:52.954Z

Authors: Julian B\\"uchel, Okan Ersoy

Published: 2018-12-04T05:24:47.000Z

## Abstract

We used the Ladder Network \[Rasmus et al. (2015)\] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. In many settings this has proven to be more successful than other semi-supervised techniques, such as pretraining using unlabeled data. We furthermore show that the convolutional Ladder Network outperforms most of the current techniques used in hyperspectral image classification and achieves new state-of-the-art performance on the Pavia University dataset given only 5 labeled data points per class.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1812.01222>)

[PDF](<https://arxiv.org/pdf/1812.01222.pdf>)