An Improved Framework for Airport Detection Under the Complex and Wide Background

Li, Ning; Cheng, Liang; Ji, Chen; Dongye, Shengkun; Li, Manchun

doi:10.1109/jstars.2022.3217040

Cited by 7 publications

(2 citation statements)

References 59 publications

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“…Therefore, these methods can be further explored in real-world scenarios. Secondly, in real-world scene classification tasks, it is sometimes necessary to differentiate between in-distribution (ID) and OOD instances within a small range, such as distinguishing between airports and roads [66]. Hence, fine-grained features could be further extracted from a fine-grained classification perspective for OOD detection.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

Li,

Jing

et al. 2024

Remote Sensing

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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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Section: Discussionmentioning

confidence: 99%

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

Li,

Jing

et al. 2024

Remote Sensing

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“…In recent years, many scholars have carried out research on object detection and extraction based on remote-sensing images [6][7][8]. At the same time, airport runway extraction has gained increasing attention in remote-sensing image processing, because airport runway information accurately extracted from remote-sensing images is very useful for airport planning [9,10], airplane extraction [11], foreign object debris detection [12,13] and military applications (such as reconnaissance) [14].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Feature Fusion-Based Method for Crater Extraction of Airport Runways in Remote-Sensing Images

Zhao,

Chen,

Gong

2024

Remote Sensing

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Due to the influence of the complex background of airports and damaged areas of the runway, the existing runway extraction methods do not perform well. Furthermore, the accurate crater extraction of airport runways plays a vital role in the military fields, but there are few related studies on this topic. To solve these problems, this paper proposes an effective method for the crater extraction of runways, which mainly consists of two stages: airport runway extraction and runway crater extraction. For the previous stage, we first apply corner detection and screening strategies to runway extraction based on multiple features of the runway, such as high brightness, regional texture similarity, and shape of the runway to improve the completeness of runway extraction. In addition, the proposed method can automatically realize the complete extraction of runways with different degrees of damage. For the latter stage, the craters of the runway can be extracted by calculating the edge gradient amplitude and grayscale distribution standard deviation of the candidate areas within the runway extraction results. In four typical remote-sensing images and four post-damage remote-sensing images, the average integrity of the runway extraction reaches more than 90%. The comparative experiment results show that the extraction effect and running speed of our method are both better than those of state-of-the-art methods. In addition, the final experimental results of crater extraction show that the proposed method can effectively extract craters of airport runways, and the extraction precision and recall both reach more than 80%. Overall, our research is of great significance to the damage assessment of airport runways based on remote-sensing images in the military fields.

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A Robust Airport Detection Method Based on Environment-Insensitive Saliency Analysis

Chen,

Zhong,

2024

Lecture Notes in Computer Science

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An Improved Framework for Airport Detection Under the Complex and Wide Background

Cited by 7 publications

References 59 publications

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

A Multi-Feature Fusion-Based Method for Crater Extraction of Airport Runways in Remote-Sensing Images

A Robust Airport Detection Method Based on Environment-Insensitive Saliency Analysis

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