.png){kind=icon}
返回
Physical-Simulation-Based Dynamic Template Matching Method for Remote Sensing Small Object Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2023.3344280.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Object detection is a fundamental challenge encountered in understanding and analyzing remote sensing images. Many current detection methods struggle to identify objects in remote sensing images due to poor feature saliency, diverse directions, and unique viewing angles of small remote sensing targets. As most remote sensing images are captured through overhead photography, the structural contour features of objects in these images remain relatively stable, such as the cross-shaped structure of the aircraft and the rectangular structure of the vehicle. Therefore, we utilize the physical simulation images as prior knowledge to supplement the invariant structural features of the target, extract the target's key geometric features through the dynamic matching method, and fuse it with the features extracted by the neural network to detect the remote sensing small target more effectively in this article. We propose a template matching method based on physical simulation images (TMSI) and add it to the modified Darknet-53 (named TMSI-Net) for small target detection. Based on this, we perform specific transformations on existing templates in terms of target scale and direction to achieve better adaptation to the corresponding goals and propose DTMSI-Net with a dynamic template library (Dynamic TMSI-Net). Experiments on several datasets demonstrate that the DTMSI-Net exhibits higher detection accuracy and more stable performance compared with the state-of-the-art methods, 4% and 2.7% higher than the second-ranked model on the VEDAI and NWPU datasets, respectively.
Keyword:
Dynamic template matching
knowledge template
physical simulation image
remote sensing object detection
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands
.png){kind=icon}
PREAI
分享收藏Crystalline‐State Reaction with Allosteric Effect in Spin‐Crossover, Interpenetrated Networks with Magnetic and Optical Bistability具有磁和光学双稳态的自旋交叉，互穿网络中具有变构效应的晶态反应
PREAI
分享收藏Pansharpening for Cloud-Contaminated Very High-Resolution Remote Sensing Images云污染的高分辨率遥感影像的泛锐化
PREAI
分享收藏Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study在嵌入式系统中使用深度学习进行基于无人机图像的实时目标检测: 以车辆检测为例
OAAI
OAAI分享收藏
分享收藏
查看更多内容