| 基于遥感图像的多模态小目标检测 |
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| 作者姓名： | 胡俊 顾晶晶 王秋红 |
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| 作者单位： | 南京航空航天大学计算机科学与技术学院，江苏 南京 210016 |
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| 摘 要： | 由于遥感图像目标往往较小且容易受光线、天气等因素的影响,所以单一模态下基于深度学习的遥感图像目标检测的准确度较低.然而,不同模态间的图像信息可以相互增强提高目标检测的性能.因此,基于RGB和红外图像,提出了一种适用于遥感图像多模态小目标检测的平衡多模态深度模型.相比简单地相加、点乘和拼接的方式融合2个模态的特征信息,设...
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| 关 键 词： | 遥感图像 平衡多模态深度模型 小目标检测 融合 VEDAI数据集 |
Multimodal small target detection based on remote sensing image |
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| Authors: | HU Jun GU Jing-jing WANG Qiu-hong |
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| Affiliation: | College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China |
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| Abstract: | Since targets in remote sensing images are relatively small and easily affected by illumination, weather, and
other factors, deep-learning based target detection methods from single modality remote sensing images suffer from
low accuracy. However, the image information between different modalities can enhance each other to improve the
performance of target detection. Therefore, based on RGB and infrared images fusion, we proposed a balanced
multimodal depth model (BMDM) for multimodal small target detection from remote sensing images. As opposed to
simple element-wise summation, element-wise multiplication, and concatenation to fuse the feature information of the
two modalities, we designed a balanced multimodal feature method to enhance target features to make up for the
shortcomings of single modal information. We first extracted low-level features from RGB and infrared images,
respectively. Secondly, we fused the feature information of the two modalities and extracted deep-level features.
Thirdly, we constructed a multimodal small target detection model based on the one-stage method. Finally, the
effectiveness of the proposed method was verified by the experimental results of multimodal small target detection
performed on the public dataset VEDAI of remote sensing images. |
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| Keywords: | remote sensing images balanced multimodal deep model small target detection fusion VEDAI dataset |
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