Effective Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion and Improved Nonmaximal Suppression Algorithm

Zhu, Ming; Xu, Yuelei; Ma, Shiping; Li, Shuai; Ma, Hua; Han, Yongsai

doi:10.3390/rs11091062

Cited by 25 publications

(14 citation statements)

References 31 publications

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“…Six anchors are set on each grid of the first two feature maps, four anchors are set on each grid of the middle two feature maps, and two anchors are set on each grid of the last two feature maps. The final setting is [6,6,4,4,2,2], which greatly reduces the number of anchors on these feature maps. The aspect ratio of Anchors is set to [1,2,3,1/2,1/3,1].…”

Section: Model Trainingmentioning

confidence: 99%

“…Its initial purpose is to extract the category and location information of the object from a remote sensing image [1]. This task involves a wide range of applications in various fields, such as remote sensing image road detection [2], ship detection [3], aircraft detection [4], etc. It is also a high-advance technique for remote sensing image analysis, image content understanding, and scene understanding.…”

Section: Introductionmentioning

confidence: 99%

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A Lightweight Object Detection Framework for Remote Sensing Images

et al. 2021

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Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

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Section: Model Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Lightweight Object Detection Framework for Remote Sensing Images

et al. 2021

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“…The effective performance of the RCNN structure in object recognition has allowed the method to become widespread in many different areas. Aircraft detection and identification systems using remote sensing images have an important place in the object recognition literature, both as conventional image processing methods are used and deep learning-based models are recommended [15]- [18]. In the study [19], as conventional methods, the Gabor filter is used in the feature extraction process and the aircraft detection process is carried out by image classification with the SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

“…In the study [19], as conventional methods, the Gabor filter is used in the feature extraction process and the aircraft detection process is carried out by image classification with the SVM classifier. Conventional methods lag behind in producing effective outputs in the bounding box display where the object is detected in a large image [15]. In studies where basic CNN forms are applied, the searching and detection algorithms performed on the satellite image for the position of the bounding boxes after the classification process also bring an additional computational cost [20].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection System Based on Regions with Convolutional Neural Networks

Uçar¹,

Dandil

Ata

2020

ijisae

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Object detection in remote sensing imagery is an important topic of image processing researches. Detection of objects and regions from satellite imagery takes its place in various applications such as the detection of residential areas and agricultural lands, road lines, ships, as well as airport and hangar detections. As a more specific remote sensing imagery based object-detection process, a stationary aircraft detection system could serve as a model in some military applications. Such a model could serve the detection of stationary aircraft targets in airports. In the proposed study, a deep learning-based model detects the aircraft in the airports using the satellite images from Google Earth. The deep learning model uses the state of the art Regions with Convolutional Neural Network (RCNN). Firstly a built from scratch CNN design is used for the basic learning step of the system. Then, RCNN performs region detection, which anchors the stationary aircraft object bounding boxes. A large dataset containing aircraft images is preferred for training of CNN. To validate the system, satellite images captured from airports in Turkey are used. The results of the study show that the proposed model successfully operates aircraft detection with high-performance rates. While the classifier network structure, which constitutes the first step of the study, produces 98.4% test accuracy, the proposed aircraft detection framework has successfully performed the aircraft identification process by producing matched bounding boxes in the test images.

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“…Aircraft detection is relatively mature compared with other targets. The many methods of aircraft detection can be roughly classified into two categories [20]: low-level features, such as edges and symmetry [21][22][23][24][25][26][27], and high-level features based on object features [20,[28][29][30][31][32][33]. For low-level features, Bo et al [21] converted RGB images to binary images for aircraft detection.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Zeng

Cheng

et al. 2019

Remote Sensing

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Airports have a profound impact on our lives, and uncovering their distribution around the world has great significance for research and development. However, existing airport databases are incomplete and have a high cost of updating. Thus, a fast and automatic worldwide airport detection method can be of significance for global airport detection at regular intervals. However, previous airport detection studies are usually based on single remote sensing (RS) imagery, which seems an overwhelming burden for worldwide airport detection with traversal searching. Thus, we propose a hierarchical airport detection method consisting of broad-scale extraction of worldwide candidate airport regions based on spatial analysis of released RS products, including impervious surfaces from FROM-GLC10 (fine resolution observation and monitoring of global land cover 10) product, building distribution from OSMs (open street maps) and digital surface model from AW3D30 (ALOS World 3D—30 m). Moreover, narrow-scale aircraft detection was initially conducted by the Faster R-CNN (regional-convolutional neural networks) deep learning method. To avoid overestimation of background regions by Faster R-CNN, a second CNN classifier is used to refine the class labeling with negative samples. Specifically, our research focuses on target airports with at least 2 km length in three experimental regions. Results show that spatial analysis reduced the possible regions to 0.56% of the total area of 75,691 km2. The initial aircraft detection by Faster R-CNN had a mean user’s accuracy of 88.90% and ensured that all the aircrafts could be detected. Then, by introducing the CNN reclassifier, the user’s accuracy of aircraft detection was significantly increased to 94.21%. Finally, through an experienced threshold of aircraft number, 19 of the total 20 airports were detected correctly. Our results reveal the overall workflow is reliable for automatic and rapid airport detection around the world with the help of released RS products. This research promotes the application and progression of deep learning.

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Effective Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion and Improved Nonmaximal Suppression Algorithm

Cited by 25 publications

References 31 publications

A Lightweight Object Detection Framework for Remote Sensing Images

A Lightweight Object Detection Framework for Remote Sensing Images

Aircraft Detection System Based on Regions with Convolutional Neural Networks

A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

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