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

doi:10.11947/j.AGCS.2019.20180431

Abstract

Abstract: 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.

Key words: remote sensing images, object detection, CNN, RBM

CLC Number:

P237

CHEN Ding, WAN Gang, LI Ke. Object detection in optical remote sensing images based on combination of multi-layer feature and context information[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10): 1275-1284.

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