Due to the trade-off between spatial resolution of the imagery and satellite revisit times, the research topics using multi-resolution 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.
