基于多尺度特征的遥感图像检测方法研究

Research on remote sensing image detection method based on multiscale features

遥感图像中的显著性目标检测在遥感信息处理领域具有重要研究价值。当前,自然场景图像的显著性目标检测技术已经取得了显著的进展。可是,遥感图像的目标尺度多样、目标种类丰富以及复杂的环境背景等特点,使得直接在遥感图像中应用自然场景图像的显著性目标检测方法变得具有挑战性。针对这些局限性,提出了一种密集连接多尺度特征网络模型,该模型充分利用神经网络的强大的特征提取能力,可以实现高效且准确的检测,并为遥感图像显著性目标检测的实际应用提供技术支持。

Significant object detection in remote sensing images has important research value in the field of remote sensing information processing.At present,the technology of salient object detection in natural scene images has made significant progress.However,the characteristics of remote sensing images,such as diverse target scales,rich target types and complex environmental background,make it challenging to directly apply the salient target detection method of natural scene images in remote sensing images.In response to these limitations,this article proposes a densely connected multi‑scale feature network model that fully utilizes the powerful feature extraction ability of neural networks,enabling efficient and accurate detection,and providing technical support for the practical application of remote sensing image saliency target detection.