# Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification

Source: [Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification](<https://www.sota2.com/research/paper/68fa6ee31e209131cea42a52>)

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

Data updated: 2025-10-26T01:08:40.604Z

Authors: Dajana Dimitri\\'c, Mitar Simi\\'c, Vladimir Risojevi\\'c

Published: 2023-03-29T12:02:18.000Z

## Abstract

Deep learning models for image classification are typically trained under the "closed-world" assumption with a predefined set of image classes. However, when the models are deployed they may be faced with input images not belonging to the classes encountered during training. This type of scenario is common in remote sensing image classification where images come from different geographic areas, sensors, and imaging conditions. In this paper we deal with the problem of detecting remote sensing images coming from a different distribution compared to the training data - out of distribution images. We propose a benchmark for out of distribution detection in remote sensing scene classification and evaluate detectors based on maximum softmax probability and nearest neighbors. The experimental results show convincing advantages of the method based on nearest neighbors.

## 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/2303.16616>)

[PDF](<https://arxiv.org/pdf/2303.16616.pdf>)