全球语义感知聚合网络用于远程传感图像中的突出物体检测
Hongli Li1,2, Xuhui Chen1,2, Wei Yang3
1School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
本研究介绍了一个全球语义意识聚合网络 (GSANet) 用于远程传感图像 (RSI) 中突出物体检测 (SOD). GSANet有效地解决了诸如阴影和不清晰的边缘等挑战,改进了地理信息分析.
科学领域:
- 计算机视觉 计算机视觉
- 地理空间分析是什么
- 遥感 遥感 遥感 遥感
背景情况:
- 在遥感图像 (RSI) 中突出物体检测 (SOD) 对于地理信息分析至关重要,但面临着诸如阴影干扰,特征混和边缘不清晰等挑战.
- 现有的方法难以准确地识别突出物体,这是由于RSI的这些固有的困难.
研究的目的:
- 开发一个有效的全球语义意识聚合网络 (GSANet) 以改善RSI中的SOD.
- 通过解决RSI中的挑战来增强突出对象的本地化和语义理解.
主要方法:
- 设计了全球语义意识聚合网络 (GSANet),利用信息来优先考虑潜在的目标地区.
- 提出了一个语义细节嵌入模块 (SDEM),用于多层次特征的自适应融合,增强突出区域信息.
- 引入了一个语义感知融合模块 (SPFM) 来分析上下文和本地细节,提高感知能力并减少语义稀释.
主要成果:
- 在ORSSD和EORSSD数据集上,GSANet表现出色.
- 在EORSSD数据集上实现了高度指标:93.91%Sα,98.36%Eξ和89.37%Fβ.
结论:
- 拟议的GSANet有效地汇总了RSI中的突出信息,优于现有的方法.
- 该网络成功地解决了用于遥感图像的SOD的关键挑战,为地理分析提供了可靠的支持.
相关概念视频
01:25Selected Data About Geographic Locations
27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
01:12Visual Agnosia
190
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
190