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Multitask learning for image translation and salient object detection from multimodal remote sensing images

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Abstract

This paper presents a novel and efficient multitask learning framework for image translation and saliency detection from remote sensing images, which mainly contains the image translation network-weight sharing attention GAN (WSA-GAN) and the salient object detection network-boundary guidance network (BGNet). WSA-GAN can be used to generate a large number of synthetic infrared remote sensing images (IRIs) or optical remote sensing images (ORIs) from the corresponding complementary modality images. Then, a new multimodal context-aware learning is proposed for feature extraction and to coordinate the entanglement of latent features in the multimodal context of ORIs and IRIs. Since convolutional neural networks do not perform well when the object has directional variance, our framework introduces the attention-aware CapsNet (AACNet) to alleviate the problem and enhance the feature expressiveness. In addition, knowledge distillation strategy is introduced in AACNet to reduce the model complexity. Finally, the multiscale feature learning network and the boundary-aware block are designed to generate more accurate saliency detection results with clear boundaries. Experimental results demonstrate that the presented image translation and salient object detection networks outperform other approaches.

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Funding

This research was funded by NSFC 61972353, NSF IIS-1816511, OAC-1910469 and Strategic Cooperation Technology Projects of CNPC and CUPB: ZLZX2020-05.

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Authors and Affiliations

  1. Beijing Key Lab of Petroleum Data Mining, Department of Computer Science and Technology, China University of Petroleum, Beijing, China

    Yuanfeng Lian, Xu Shi & ShaoChen Shen

  2. Wayne State University, Detroit, MI, USA

    Jing Hua

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Lian, Y., Shi, X., Shen, S. et al. Multitask learning for image translation and salient object detection from multimodal remote sensing images. Vis Comput 40, 1395–1414 (2024). https://doi.org/10.1007/s00371-023-02857-3

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