Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification Article Swipe
Sicong Li
,
Ning Li
,
Min Jing
,
Chen Ji
,
Liang Cheng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16091501
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16091501
Although deep neural networks have made significant progress in tasks related to remote sensing image scene classification, most of these tasks assume that the training and test data are independently and identically distributed. However, when remote sensing scene classification models are deployed in the real world, the model will inevitably encounter situations where the distribution of the test set differs from that of the training set, leading to unpredictable errors during the inference and testing phase. For instance, in the context of large-scale remote sensing scene classification applications, it is difficult to obtain all the feature classes in the training phase. Consequently, during the inference and testing phases, the model will categorize images of unidentified unknown classes into known classes. Therefore, the deployment of out-of-distribution (OOD) detection within the realm of remote sensing scene classification is crucial for ensuring the reliability and safety of model application in real-world scenarios. Despite significant advancements in OOD detection methods in recent years, there remains a lack of a unified benchmark for evaluating various OOD methods specifically in remote sensing scene classification tasks. We designed different benchmarks on three classical remote sensing datasets to simulate scenes with different distributional shift. Ten different types of OOD detection methods were employed, and their performance was evaluated and compared using quantitative metrics. Numerous experiments were conducted to evaluate the overall performance of these state-of-the-art OOD detection methods under different test benchmarks. The comparative results show that the virtual-logit matching methods without additional training outperform the other types of methods on our benchmarks, suggesting that additional training methods are unnecessary for remote sensing image scene classification applications. Furthermore, we provide insights into OOD detection models and performance enhancement in real world. To the best of our knowledge, this study is the first evaluation and analysis of methods for detecting out-of-distribution data in remote sensing. We hope that this research will serve as a fundamental resource for future studies on out-of-distribution detection in remote sensing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16091501
- https://www.mdpi.com/2072-4292/16/9/1501/pdf?version=1713963603
- OA Status
- gold
- Cited By
- 4
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395082323
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4395082323Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs16091501Digital Object Identifier
- Title
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Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-24Full publication date if available
- Authors
-
Sicong Li, Ning Li, Min Jing, Chen Ji, Liang ChengList of authors in order
- Landing page
-
https://doi.org/10.3390/rs16091501Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/16/9/1501/pdf?version=1713963603Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-4292/16/9/1501/pdf?version=1713963603Direct OA link when available
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Computer science, Artificial intelligence, Inference, Contextual image classification, Context (archaeology), Machine learning, Remote sensing, Data mining, Pattern recognition (psychology), Image (mathematics), Biology, Paleontology, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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