A Remote Sensing Scene Classification Method Based on Object-aware Multi-code Generator
This paper introduces an object-aware multi-code generator network approach for remote sensing scenes from two aspects,which are multiple instance object localization and generative adversarial networks.First,the original image as well as the fake image generated by the generative adversarial network are used as input to the joint localization module to exploit the differenc-es and complementarities in their features in order to localize multiple informative object parts.Then,a multi-coding generator net-work is proposed,where each latent code is used to recover a specific region of the image while combining these latent codes by channel importance weights in the middle layer of the generator to synthesize a fake image with sufficient detail.Finally,this paper combines object part-level classification and image global-level classification as the final classification results for remote sensing scene classification.The method in this paper is mainly validated on two publicly available large-scale scene datasets,AID and NW-PU-RESISC45.Compared with existing methods,this method effectively locates multiple informative object parts and improves the performance of remote sensing scene classification.
remote sensing scene classificationmultiple instance parts localizationgenerative adversarial network
国家科技期刊平台
NSTL
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