基于显著性特征和角度信息的遥感图像目标检测

遥感图像中的目标具有密集性、多尺度和多角度等特性,这使得遥感图像多类别目标检测成为一项具有挑战性的课题。因此,文中提出了一种新的端到端的遥感图像目标检测框架。该框架通过提取显著性特征和不同卷积通道之间的相互关系来增强目标信息,抑制非目标信息,从而提高特征的表示能力。同时,在不增加模型参数的情况下,在卷积模块中添加多尺度特征模块来捕获更多的上下文信息。为了解决遥感图像中目标角度多变这一问题,该框架在区域建议网络中加入了角度信息,得到有角度的矩形候选框,并在训练过程中添加注意力损失函数来引导网络学习显著性特征。该框架在公开的遥感图像数据集上进行了相关验证,在水平任务框和方向任务框上的实验结果证明了所提方法的有效性。

Object Detection in Remote Sensing Images Based on Saliency Feature and Angle Information

Multi-class object detection of remote sensing images is a challenging subject since objects in remote sensing images are dense,multi-oriented and multi-scale.This paper presents a new framework for object detection in remote sensing images.The framework enhances object information,suppresses non-object information,and improves the ability of feature representation by extracting salient features and the relationship between different convolutional channels.Meanwhile,multi-scale features are added to the convolution module to capture more context information without adding extra parameters to the model.To solve the problem of variable object angle in rencote sensing image,we add angle information to the Region Proposal Network(RPN)to get oriented rectangular object proposals.In the training stage,the attention loss function is added to guide the significance of network learning.The proposed framework is validated on the public remote sensing image data set,and the experimental results on the horizontal boxes task and the oriented boxes task prove the effectiveness of the proposed method.