Aircraft Target Detection from Spaceborne SAR Image

Guo, Qian; Wang, Haipeng; Kang, Lihong; Zhou, Li; Xu, Feng

doi:10.1109/igarss.2019.8898548

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…In recent years, deep learning has been able to automatically learn in the field of target identification in remote sensing images [19][20][21]; it has been widely used to identify targets in ultra-high-resolution remote sensing images, and significant results have been achieved [22][23][24]. Compared with traditional machine learning algorithms such as support vector machines (SVMs) [25], deep learning has been recognized as a high-precision identification method [26].…”

Section: Introductionmentioning

confidence: 99%

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Qin

Wang

et al. 2021

Remote Sensing

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Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

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Section: Introductionmentioning

confidence: 99%

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Qin

Wang

et al. 2021

Remote Sensing

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“…When there are 400 negative samples, the data show that the detection accuracy is at its highest. Feng Xu, j. et al [10], an end-to-end aircraft detection algorithm for large scene spaceborne synthetic aperture radar (SAR) imagery is put forth in this study. The results of the experiments demonstrate that the suggested strategy produces good results at a reasonable cost of computation.…”

Section: Literature Review C Chen [1]mentioning

confidence: 99%

Real Time Web-based System to Detect Military Aircraft Using RESNET-50 Algorithm

Sudhakar¹,

Deekshitha²,

Kummari³

et al. 2023

EAE

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As target detection in remote sensing imaging depends on aircraft type recognition, it is essential in both civil and military applications. The job is made more difficult by the existence of fine-grained features, which can result in significant intra-class changes due to variations in size, posture, and angle, as well as modest inter class changes due to very similar subcategories. This kind of system can be helpful for military security as recognition of the type of aircraft is very critical to the decisions being made. There are several existing ways which uses methods like Radar System and Radio footprints, Speed etc., to detect type of Aircraft. Although these methods are massively costly and still cannot detect the type of Aircraft accurately. In this paper aircraft is detected using ResNet-50, Advance State of Art Object Detection Algorithm implementing in Anaconda tool with train accuracy is 98% & validate accuracy is 75%. A crucial area of artificial intelligence is object detection, which enables computer systems to perceive their surroundings by identifying things in visual pictures or movies. In case of any dangerous Aircraft, the system will have capability to raise alarm and Alert using Audio Sirens. The software requirement for this project is python, 3.6/anaconda, or newer and necessary python modules.

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“…The Hough transform was used to extract the skeleton composed of the wings and the fuselage, and the other parts of aircraft were identified based on the collinearity of aircraft structure and symmetry. Guo et al [5] used edge detection algorithms based on the Canny operator to extract the candidate slices of the aircraft target. Textures are another set of key features commonly extracted from SAR images to describe visual properties using directional gradient distribution and visual saliency [6].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

Zhang,

Hu,

et al. 2023

Remote Sensing

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Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks.

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Aircraft Target Detection from Spaceborne SAR Image

Cited by 13 publications

References 6 publications

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Real Time Web-based System to Detect Military Aircraft Using RESNET-50 Algorithm

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

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