多层特征与上下文信息相结合的光学遥感影像目标检测

Object detection in optical remote sensing images based on combination of multi-layer feature and context information

目标检测是遥感影像分析的基础和关键。针对光学遥感影像中目标尺度多样、小目标居多、相似性及背景复杂等问题,本文提出一种将卷积神经网络(CNN)和混合波尔兹曼机(HRBM)相结合的遥感影像目标检测方法。首先设计细节—语义特征融合网络(D-SFN)提取卷积神经网络低层和高层融合特征,提升目标特征的判别力,特别是小目标;其次考虑上下文信息对目标检测的影响,结合上下文信息进一步加强目标表征的准确性,提升检测精度。在NWPU数据集上试验表明,本文方法能够显著提升目标检测精度且具有一定程度的稳健性。

Object detection is the basic and key step of remote sensing image analysis. In optical remote sensing images, object detection faced many challenges such as multi-scale and small objects, appearance ambiguity and complicated background. To address these problems, a new method of object detection based on convolutional neural networks (CNN) and hybrid restricted boltzmann machine (HRBM) is proposed. Firstly, the detail-semantic feature fusion network (D-SFN) is designed to extract fusion features from low-level and high-level CNNs, which can make the target representation more distinguishable, especially for small objects. Secondly, context information is incorporated to further boost feature discrimination, which also improves the detection accuracy. Experiments on NWPU datasets show that the proposed method can significantly improve the accuracy of object detection and has certain robustness.