面向精细化多尺度特征的遥感图像目标检测

张省; 李山山; 魏国芳; 张新耐; 高建威

doi:10.11834/jrs.20221801

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面向精细化多尺度特征的遥感图像目标检测
Refined multi-scale feature-oriented object detection of the remote sensing images
2022年26卷第12期页码：2616-2628
纸质出版日期： 2022-12-07 ，
DOI： 10.11834/jrs.20221801

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张省，李山山，魏国芳，张新耐，高建威.2022.面向精细化多尺度特征的遥感图像目标检测.遥感学报，26（12）： 2616-2628

Zhang S， Li S S， Wei G F， Zhang X N and Gao J W. 2022. Refined multi-scale feature-oriented object detection of remote sensing images. National Remote Sensing Bulletin， 26（12）：2616-2628
张省，李山山，魏国芳，张新耐，高建威.2022.面向精细化多尺度特征的遥感图像目标检测.遥感学报，26（12）： 2616-2628 DOI： 10.11834/jrs.20221801.

Zhang S， Li S S， Wei G F， Zhang X N and Gao J W. 2022. Refined multi-scale feature-oriented object detection of remote sensing images. National Remote Sensing Bulletin， 26（12）：2616-2628 DOI： 10.11834/jrs.20221801.

摘要

遥感图像目标检测是对目标视觉特征的描述与图像先验知识的表达，解译得到的信息无论在军事领域还是在民用领域都有着广泛的应用。针对复杂场景下遥感图像目标特征提取能力不足，目标尺度差异较大、方向任意且紧密排列，传统目标检测所使用的水平框难以准确定向等问题，提出了一种精细化多尺度特征的遥感图像定向目标检测算法。首先，设计了一种基于空洞卷积的上下文注意力网络，能够利用不同空洞率的卷积核捕获局部和全局语义信息，并利用注意力机制将语义信息整合到原始特征上，提升目标特征提取能力；其次，提出了一个精细化的特征金字塔网络，通过像素混洗的方式减少特征金字塔中的通道信息损失，强化网络对差异性大的多尺度目标特征信息的理解能力；最后，研究利用滑动顶点的方式回归定向的矩形框，更好地表示遥感图像内有向目标的位置。本文以Fast R-CNN OBB为基准，通过在目标检测公开数据集DOTA和HRSC2016上验证了算法的有效性，结果显示本文算法在DOTA数据集上与基准算法比较，平均精度（mAP）提升了22.65%，最终检测精度mAP达到了76.78%。在HRSC2016数据集上，最终检测精度mAP达到了89.95%。此外，本文算法较多种先进算法相比均有具有较好的提升。

Abstract

Object detection of remote sensing image is the description of visual features of the object and the expression of the image prior knowledge

and the information obtained by the interpretation has a wide range of applications in both military and civilian fields. A refined multi-scale feature-oriented object detection of remote sensing image is proposed to address the problems of insufficient feature extraction capability of remote sensing image objects in complex scenes

large variations in object scales

arbitrary and closely arranged directions

and difficulties in the accurate orientation of horizontal frames used in traditional object detection.

First

a contextual attention network based on dilated convolution is designed

which can capture local and global semantic information by using convolution kernels with different dilated rates and integrate semantic information into the original features utilizing an attention mechanism to enhance feature extraction. Second

a refined feature pyramid network is proposed to reduce the loss of channel information in the feature pyramid by pixel shuffling and strengthen the network’s ability to understand multi-scale object feature information with large variances. Finally

the study uses gliding vertices to regress the oriented rectangular box to represent the location of directed objects within remote sensing images.

In this work

the effectiveness of the algorithm is verified by using Fast R-CNN OBB as a baseline on the object detection public datasets DOTA and HRSC2016. Results show that the algorithm in this work improves the mean average precision (mAP) by 22.65% on the DOTA dataset compared with the baseline. The final detection accuracy mAP reaches 76.78%. The final detection accuracy mAP on the HRSC2016 dataset reached 89.95%. In addition

the algorithm in this work has a better improvement compared with the various advanced algorithms.

Conclusion

First

the contextual attention network with dilated convolution is used to strengthen the object features

which enhances the discriminative ability of the convolutional neural network for objects and backgrounds in remote sensing images. Second

the refined feature pyramid is used to solve the problem of large variation of objects in remote sensing images. Finally

the direction factor of gliding vertices is introduced to represent the oriented objects

which reduces the regression boundedness problem that can be brought by angle regression.

关键词

遥感深度学习目标检测特征提取多尺度特征金字塔定向回归框

Keywords

remote sensingdeep learningobject detectionfeature extractionmulti-scale feature pyramidoriented bounding box

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