Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images

Wu, Qingxian; Feng, Daqiang; Cao, Chuanbao; Zeng, Xiaodong; Feng, Zhouming; Wan, Jin; Huang, Ziqiang

doi:10.3390/s21082618

Cited by 49 publications

(21 citation statements)

References 25 publications

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“…This section presents the experimental results in terms of the experimental setup and performance results for the SVM-RBF and SVM-Linear, NDCI [ 8 ], HSRS [ 19 ], SCMask R-CNN [ 17 ], CIAs [ 18 ], KCA [ 21 ], AOPC [ 22 ], MLC [ 33 ] and MDC [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

“…Some researchers have focused for the most part on the evaluation of parametric classifiers. Improved Mask of Recurrent Neural Network is introduced remote sensing images [ 17 ]. The proposed approach is called as SCMask R-CNN, and the goal of this proposed approach is to enhance the detection effect by providing a higher resolution of remote image sensing.The proposed approach also provided segmentation and object recognition concurrently.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SVM-RBF, SVM-Linear, MLC, and MDC have been compared both from a mathematical perspective and with the experiment results to show their different levels of accuracy and efficiency. Moreover, SVM-RBF, SVM-Linear have also been compared with current state-of-the-art algorithms: NDCI [ 8 ], SCMask R-CNN [ 17 ], CIAs [ 18 ], KCA [ 21 ], and AOPC [ 22 ] from the change detection accuracy, and reliability viewpoint.…”

Section: Literature Reviewmentioning

confidence: 99%

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Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

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Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Razaque

Frej

Almiani

et al. 2021

Sensors

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“…The mask R-CNN is a versatile model used in different fields [21] and comprises two phases: region proposals generation and classification [51]. This paper adopted the mask R-CNN as the benchmark model for detecting the footprints of rural buildings in dense areas.…”

Section: Proposed Methodsmentioning

confidence: 99%

Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN

Mohamed¹,

Mahmoud²,

Moustafa³

et al. 2022

IJACSA

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Building footprint extraction is an essential process for various geospatial applications. The city management is entrusted with eliminating slums, which are increasing in rural areas. Compared with more traditional methods, several recent research investigations have revealed that creating footprints in dense areas is challenging and has a limited supply. Deep learning algorithms provide a significant improvement in the accuracy of the automated building footprint extraction using remote sensing data. The mask R-CNN object detection framework used to effectively extract building in dense areas sometimes fails to provide an adequate building boundary result due to urban edge intersections and unstructured buildings. Thus, we introduced a modified workflow to train ensemble of the mask R-CNN using two backbones ResNet (34, 101). Furthermore, the results were stacked to fine-grain the structure of building boundaries. The proposed workflow includes data preprocessing and deep learning, for instance, segmentation was introduced and applied to a light detecting and ranging (LiDAR) point cloud in a dense rural area. The outperformance of the proposed method produced better-regularized polygons that obtained results with an overall accuracy of 94.63%.

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“…Wu et al [ 13 ] used the Edge Boxes algorithm to generate region proposals and used the CNN model to extract features and classify them. Wu et al [ 14 ] enhanced the detection effect by adding improved self-calibrated convolution and dilated convolution into the Mask R-CNN framework. Luo et al [ 15 ] proposed the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module, which were systematically integrated into the YOLOv5 base network to improve the aircraft detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Chen

Liu

et al. 2022

Sensors

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Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

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Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images

Cited by 49 publications

References 25 publications

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification

Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

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