A Feature Fusion Airport Detection Method Based on the Whole Scene Multispectral Remote Sensing Images

Dong, Xinyu; Tian, Jia; Tian, Qingjiu

doi:10.1109/jstars.2021.3139926

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…We chose airports as the detection object. Due to the large dynamic range in remote sensing images, we chose the airport detection algorithm based on deep learning [16] to obtain object data.…”

Section: Experimental Results Analysismentioning

confidence: 99%

PAN source image data reconstruction based on data hiding

Liu,

Zhou,

Zheng

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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Although the fusion of Panchromatic(PAN) images and Multi-spectral(MS) images can improve the resolution of remote sensing MS images, the joint adjustments of PAN images and MS images will lead to the original data being completely lost. With the requirement of on-orbit image processing and multiple applications, obtaining fusion images while retaining the source images has become a problem that must be considered. To deal with the problem that the original data is lost after fusion, this proposed method embeds the PAN image object and compression data into the fused image. The experimental results show that the proposed method can reconstruct the object data of the PAN image losslessly and reconstruct the entire PAN image in high quality. The fused image indicators also have no impact.

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Section: Experimental Results Analysismentioning

confidence: 99%

PAN source image data reconstruction based on data hiding

Liu,

Zhou,

Zheng

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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show abstract

“…detection, such as SSD, YOLO, R-CNN, and Faster R-CNN (He et al, 2016;Li et al, 2019Li et al, , 2021Zhong et al, 2020;Fan et al, 2021;Tu et al, 2021;Dong et al, 2022;Mikriukov et al, 2022). For instance, Lawal (2021) have proposed a modified YOLOv3 model to detect tomatoes in complex environments.…”

mentioning

confidence: 99%

Small target detection with remote sensing images based on an improved YOLOv5 algorithm

Pei

Shi²,

Gong

2023

Front. Neurorobot.

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IntroductionSmall target detection with remote sensing images is a challenging topic due to the small size of the targets, complex, and fuzzy backgrounds.MethodsIn this study, a new detection algorithm is proposed based on the YOLOv5s algorithm for small target detection. The data enhancement strategy based on the mosaic operation is applied to expand the remote image training sets so as to diversify the datasets. First, the lightweight and stable feature extraction module (LSM) and C3 modules are combined to form the feature extraction module, called as LCB module, to extract more features in the remote sensing images. Multi-scale feature fusion is realized based on the Res 2 unit, Dres 2, and Spatial Pyramid Pooling Small (SPPS) models, so that the receptive field can be increased to obtain more multi-scale global information based on Dres2 and retain the obtained feature information of the small targets accordingly. Furthermore, the input size and output size of the network are increased and set in different scales considering the relatively less target features in the remote images. Besides, the Efficient Intersection over Union (EIoU) loss is used as the loss function to increase the training convergence velocity of the model and improve the accurate regression of the model.Results and discussionThe DIOR-VAS and Visdrone2019 datasets are selected in the experiments, while the ablation and comparison experiments are performed with five popular target detection algorithms to verify the effectiveness of the proposed small target detection method.

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“…Authors in [29] presented a method by combining spectral features and geometric features of airports to cope with complex background challenges. In this way, a decision tree algorithm was developed based on these features to extract the main concrete areas within the whole RSI.…”

Section: Literature Reviewmentioning

confidence: 99%

EYNet: Extended YOLO for Airport Detection in Remote Sensing Images

Mirhajianmoghadam¹,

Haghighi²

2022

Preprint

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Nowadays, airport detection in remote sensing images has attracted considerable attention due to its strategic role in civilian and military scopes. In particular, uncrewed and operated aerial vehicles must immediately detect safe areas to land in emergencies. The previous schemes suffered from various aspects, including complicated backgrounds, scales, and shapes of the airport. Meanwhile, the rapid action and accuracy of the method are confronted with significant concerns. Hence, this study proposes an effective scheme by extending YOLOV3 and ShearLet transform. In this way, MobileNet and ResNet18, with fewer layers and parameters retrained on a similar dataset, are parallelly trained as base networks. According to airport geometrical characteristics, the ShearLet filters with different scales and directions are considered in the first convolution layers of ResNet18 as a visual attention mechanism. Besides, the major extended in YOLOV3 concerns the detection Sub-Networks with novel structures which boost object expression ability and training efficiency. In addition, novel augmentation and negative mining strategies are presented to significantly increase the localization phase's performance. The experimental results on the DIOR dataset reveal that the framework reliably detects different types of airports in a varied area and acquires robust results in complex scenes compared to traditional YOLOV3 and state-of-the-art schemes.

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A Feature Fusion Airport Detection Method Based on the Whole Scene Multispectral Remote Sensing Images

Cited by 5 publications

References 43 publications

PAN source image data reconstruction based on data hiding

PAN source image data reconstruction based on data hiding

Small target detection with remote sensing images based on an improved YOLOv5 algorithm

EYNet: Extended YOLO for Airport Detection in Remote Sensing Images

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