# Out-of-distribution detection in satellite image classification

arXiv: https://arxiv.org/abs/2104.05442 · HTML: https://mlsanity.com/paper/2104.05442/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples](https://mlsanity.com/paper/2010.10474/) (2020)
- [So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification](https://mlsanity.com/paper/1912.12171/) (2019)
- [Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification](https://mlsanity.com/paper/2103.08741/) (2021)

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

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification](https://mlsanity.com/paper/2303.16616/) (2023)
- [OT on the Map: Quantifying Domain Shifts in Geographic Space](https://mlsanity.com/paper/2604.16220/) (2026)
- [Reliability Quantification of Deep Reinforcement Learning-based Control](https://mlsanity.com/paper/2309.16977/) (2023)

## Abstract (arXiv)

In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area. Deep learning based models may behave in unexpected manner when subjected to test data that has such distributional shifts from the training data, also called out-of-distribution (OOD) examples. Predictive uncertainly analysis is an emerging research topic which has not been explored much in context of satellite image analysis. Towards this, we adopt a Dirichlet Prior Network based model to quantify distributional uncertainty of deep learning models for remote sensing. The approach seeks to maximize the representation gap between the in-domain and OOD examples for a better identification of unknown examples at test time. Experimental results on three exemplary test scenarios show the efficacy of the model in satellite image analysis.