Multi-resolution domain adaptation via multiple instance learning for improving the recognition accuracy of Japanese oak wilt in low-resolution satellite imagery

Mitsuyoshi Otsu; Sho Nakamura; Shigeru Tomita; Tomoyuki Suhama; Yasunobu Shimazaki; Katsuya Nishimura

doi:10.1117/12.2643217

22 May 2023 Multi-resolution domain adaptation via multiple instance learning for improving the recognition accuracy of Japanese oak wilt in low-resolution satellite imagery

Mitsuyoshi Otsu, Sho Nakamura, Shigeru Tomita, Tomoyuki Suhama, Yasunobu Shimazaki, Katsuya Nishimura

Author Affiliations +

Proceedings Volume 12327, SPIE Future Sensing Technologies 2023; 1232716 (2023) https://doi.org/10.1117/12.2643217
Event: SPIE Future Sensing Technologies, 2023, Yokohama, Japan

Abstract

Due to the trade-off between spatial resolution of the imagery and satellite revisit times, the research topics using multiresolution remote sensing data have attracted much attention. In recent years, the techniques for improving the visibility of multi-resolution imagery have been proposed, including pan-sharpening and super-resolution. However, there are relatively few studies on the techniques of improving the model performance on low-resolution imagery by referring to detailed information from the high-resolution imagery during training time. To tackle this type of task, domain adaptation has been proposed in the field of computer vision to adapt a model trained on one dataset to another with different properties. Yet, domain adaptation for multi-resolution data is difficult due to the scale variation in addition to differences from the camera sensors. In this study, we propose a new approach for multi-resolution modeling that combines the major techniques, semi-supervised domain adaptation (SSDA) and multiple instance learning (MIL). Under the MIL framework, a large scene can be regarded as a bag of instances (e.g., image patches), and information from different receptive field sizes can be exploited. We conducted experiments on a dataset of Japanese oak wilt, which is known to have severe forest damage with two different optical satellite imagery, SPOT6&7 satellite imageries (1.5m) and Pleiades-1B satellite imagery (0.5m). The proposed method improves the discrimination accuracy of the low-resolution model compared to the standard SSDA technique. This obtained result reveals the potential usefulness of MIL for effective multi-resolution modeling.

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Mitsuyoshi Otsu, Sho Nakamura, Shigeru Tomita, Tomoyuki Suhama, Yasunobu Shimazaki, and Katsuya Nishimura "Multi-resolution domain adaptation via multiple instance learning for improving the recognition accuracy of Japanese oak wilt in low-resolution satellite imagery", Proc. SPIE 12327, SPIE Future Sensing Technologies 2023, 1232716 (22 May 2023); https://doi.org/10.1117/12.2643217

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