CRABR-Net:一个基于注意力的上下文关系识别网络,用于远程传感场景目标
Ningbo Guo1, Mingyong Jiang1, Lijing Gao2
1Space Information Academic, Space Engineering University, Beijing 101407, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了CRABR-Net用于远程传感场景客观识别 (RSSOR). 该网络有效地利用卷积神经网络 (CNN) 层之间的关系来显著提高识别精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 遥感场景客观识别 (RSSOR) 对于军事和民用应用至关重要.
- 卷积神经网络 (CNN) 已经推进了RSSOR,但当前的方法往往忽略了层间特征关系,导致准确度低于最佳.
- 现有的基于CNN的RSSOR技术经常只使用最后一层的特征或简单的融合方法,导致特征冗余和损失.
研究的目的:
- 开发一种基于CNN的先进网络,用于高分辨率的远程传感场景客观识别.
- 解决现有的RSSOR方法中特征融合和层间关系利用的局限性.
- 增强功能学习能力,以便在遥感图像中更准确,更强大的目标识别.
主要方法:
- 介绍了基于情境的,关系的注意力识别网络 (CRABR-Net).
- 使用无参数注意模块 (SimAM) 专注于来自不同 CNN 层的突出特征内容.
- 实现了互补和增强的关系特征地图计算,以有效地融合相邻的特征地图并改善特征学习.
- 为了最终的RSSOR,利用了来自多层的连接特征地图.
主要成果:
- CRABR-Net有效地利用不同CNN层之间的关系来提高识别性能.
- 拟议的网络与几个最先进的算法相比,取得了更好的结果.
- 实现了高平均准确率:在AID上达到96.46%,在UC-Merced上达到99.20%,在RSSCN7上达到95.43%,具有标准培训比率.
结论:
- 对于RSSOR,CRABR-Net展示了利用CNN中层间特征关系的显著好处.
- 新的功能融合和注意力机制有助于提高遥感图像分析的准确性和效率.
- 该网络提供了一种有希望的方法,用于提高高分辨率遥感场景中的客观识别智能.
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