Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

Zeng, Beichen; Ming, Dongping; Ji, Fengcheng; Yu, Jirong; Xu, Lu; Zhang, Liang; Lian, Xinyi

doi:10.1080/01431161.2022.2034194

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…With the advancement of deep learning technology [29], [30], [31], [32], [33], several researchers have tried to apply deep learning models to building recognition and extraction [34], [35], [36]. Previously, the OCNN classification method is generated according to deep learning classification combined with the object-based image segmentation method.…”

Section: A Building Extraction From Hrs Imagesmentioning

confidence: 99%

Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

Dong

Ming

Weng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Building extraction from remote sensing imagery has been a research hotspot for some time with the advancement of AI in remote sensing. However, the edges of buildings extracted using existing techniques are commonly broken and inaccurate for the complex scenes in suburban and rural areas. This study proposes a framework for extracting structures by combining region-line feature fusion with object-based convolutional neural networks to solve this problem. First, a building edge detection network known as the Multichannel Attention-based Dense Extreme Inception Network for Edge Detection (MA-DexiNed) is constructed, which is considered more accurate for building edge extraction in complicated image scenes. Second, the probability map of the building edges obtained by MA-DexiNed is refined. According to rule judgment, breakpoints are linked by an edge thinning connection algorithm to obtain single-pixel, contiguous building line features. Third, the geometric boundaries of buildings are obtained by combining region attributes derived by unsupervised image segmentation and line features obtained from deep learning supervised segmentation. Finally, the pretrained AlexNet is employed to identify the class characteristics of buildings. The suggested framework was used for two GF-2 images and one Google Earth image from various regions and with numerous types of complicated scenes.The experimental findings demonstrated that this approach could extract more precise and complete building edges for complex image scenes compared with several existing methods. This advancement results from constrained regional image segmentation using deep semantic edge features. This methodology can offer a benchmark for subsequent building extraction tasks from high resolution imagery.

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Section: A Building Extraction From Hrs Imagesmentioning

confidence: 99%

Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

Dong

Ming

Weng

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…And IoU criterion has a huge impact on NMS, which is not conducive to the accurate extraction of objects. Some research use 2 > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < average precision (AP) to evaluate the results [23,24]. In addition, using the OBB for object detection requires the additional design of the anchors and loss function, which intensifies the computational burden.…”

Section: Introductionmentioning

confidence: 99%

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Xu,

Yu,

Ming

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object detection from remote sensing images is a key technology for earth observation applications, which has important scientific research value. Ground objects in remote sensing images appear at arbitrary angles. However, object detection based on horizontal bounding boxes (HBBs) would cause mutual coverage among targets while the ground objects were dense-distributed or with a large aspect ratio. The oriented object detection methods could solve the problem by predicting the rotation angle. But the currently used methods were timeconsuming in labelling and require complex loss functions of networks. Thus, this paper combined HBB and oriented object detection to achieve rectangular object detection. Firstly, object detection network with HBBs is trained and the model predicts results in original remote sensing image. Secondly, the rotation angles are derived from the line detection with linear Hough transform on the cropped region of targets obtained by the first step. Then, the original image is rotated according to the rotation angles and detects object with HBBs again. Finally, new bounding boxes are mapped to the original image to get the detection results with oriented bounding boxes (OBBs).Experiments on public remote sensing datasets demonstrate that the proposed method is highly flexible and can be combined with any HBBs object detection network. The idea of original image rotation does not use the OBB labels to retrain the network, which reduces the workload of object annotations. Additionally, the method can be used to automatically generate OBBs to label rectangular objects since the public datasets commonly annotated by HBBs.

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“…Wu et al [23] proposed a selfcalibrated Mask R-CNN model that performs perform object recognition and segmentation in parallel. Zeng et al [24] utilize a top-down approach for aircraft detection in large scenes. Once the airport area is extracted with U-Net, Faster-RCNN with a feature enhancement module is applied for target detection.…”

Section: Introductionmentioning

confidence: 99%

A benchmark dataset for deep learning-based airplane detection: HRPlanes

Bakırman

Sertel

2023

International Journal of Engineering and Geosciences

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Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.

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Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

Cited by 6 publications

References 45 publications

Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

A benchmark dataset for deep learning-based airplane detection: HRPlanes

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