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DVF-NET:基于移位矢量场融合的双时空遥感图像注册网络

Mingliang Xue¹, Yiming Zhang², Shucai Jia¹

¹SEAC Key Laboratory of Big Data Applied Technology, College of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China.

Sensors (Basel, Switzerland)

|March 17, 2025

概括

本研究介绍了DVF-NET,这是一个用于精确远程传感图像记录的深度学习框架. 它有效地处理复杂的扭曲,提高了多时间分析和环境监测的准确性.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
地理空间分析的研究.

背景情况:

准确的图像记录对于多时空遥感分析至关重要.
图像之间的非线性扭曲对精确对齐构成重大挑战.

研究的目的:

引入DVF-NET,这是一个新的深度学习框架,用于双时间遥感图像注册.
提高图像注册的准确性和稳定性,特别是对于具有大量几何扭曲的图像.

主要方法:

开发了DVF-NET,这是一个深度学习框架,集成了两个位移矢量场.
整合了一个结构注意模块 (SAT),用于增强特征提取.
提出了一种新的损失函数,将多个相似度指标结合起来,用于全面的培训监督.

主要成果:

与各种遥感数据集的现有方法相比,DVF-NET显示出更高的准确性和稳定性.
该框架有效地处理了显著的几何扭曲,例如倾斜的建筑物.
对变化检测,土地覆盖分类和环境监测等应用的验证有效性.

结论:

在远程传感图像注册技术方面,DVF-NET提供了一个有前途的进步.
该方法为复杂的现实场景提供了高精度和稳定性.
突出了改善多时态图像分析和地理空间应用的潜力.

关键词:

移位向量场的移位向量场是指移位的向量场.双时间图像注册图像注册结构性的注意力.

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