Remote sensing aircraft detection method based on lightweight YOLOv4

2021 ◽  
Author(s):  
Fei Cheng ◽  
Huanxin Zou ◽  
Xu Cao ◽  
Runlin Li ◽  
Shitian He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Aircraft Detection

Fast aircraft detection method in optical remote sensing images based on deep learning

2021 ◽  
Vol 15 (01) ◽  
Author(s):  
Zhi-Feng Xu ◽  
Rui-Sheng Jia ◽  
Jin-Tao Yu ◽  
Jian-Zhi Yu ◽  
Hong-Mei Sun
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Detection Method ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Aircraft Detection

scholarly journals Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liming Zhou ◽  
Haoxin Yan ◽  
Chang Zheng ◽  
Xiaohan Rao ◽  
Yahui Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
High Rate ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Aircraft Detection ◽  
Low Rate

Aircraft, as one of the indispensable transport tools, plays an important role in military activities. Therefore, it is a significant task to locate the aircrafts in the remote sensing images. However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy and high rate of missed detection. To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed to detect aircraft targets in sophisticated environments. On the fundamental of the YOLOv3 detection framework, this method adds a feature fusion module to enrich the details of the feature map by mixing the shallow features with the deep features together. Experimental results on the RSOD-DataSet and NWPU-DataSet indicate that the new method raised in the article is capable of improving the problems of low rate of detection accuracy and high rate of missed detection. Meanwhile, the AP for the aircraft increases by 1.57% compared with YOLOv3.

Efficient Maize Tassel-Detection Method using UAV based Remote Sensing

2021 ◽  
pp. 100549
Author(s):  
Ajay Kumar ◽  
Sai Vikas Desai ◽  
Vineeth N. Balasubramanian ◽  
P. Rajalakshmi ◽  
Wei Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Maize Tassel

scholarly journals Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4673-4687
Author(s):  
Jixiang Zhao ◽  
Shanwei Liu ◽  
Jianhua Wan ◽  
Muhammad Yasir ◽  
Huayu Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Evidence Theory ◽  
Remote Sensing Image

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

2021 ◽  
Vol 13 (18) ◽  
pp. 3652
Author(s):  
Duo Xu ◽  
Yixin Zhao ◽  
Yaodong Jiang ◽  
Cun Zhang ◽  
Bo Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
High Efficiency ◽  
Detection Method ◽  
Laplacian Operator ◽  
Ground Fissures ◽  
Aerial Vehicle ◽  
Edge Detection Method

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 a.m. and 5:00 a.m. under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 a.m., 1:00 p.m. and 3:00 p.m.

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